WorldWideScience

Sample records for text mining technology

  1. Science and Technology Text Mining: Wireless LANS

    Science.gov (United States)

    2005-01-01

    Page 1 SCIENCE AND TECHNOLOGY TEXT MINING : WIRELESS LANS By Dr. Ronald N. Kostoff Office of Naval Research 874 North Randolph...Minnesota) KEYWORDS: Wireless LANs; Database Tomography; text mining ; clustering; computational linguistics; bibliometrics; scientometrics...Technology Text Mining : Wireless LANS 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER

  2. Science and Technology Text Mining Basic Concepts

    National Research Council Canada - National Science Library

    Losiewicz, Paul

    2003-01-01

    ...). It then presents some of the most widely used data and text mining techniques, including clustering and classification methods, such as nearest neighbor, relational learning models, and genetic...

  3. Science and Technology Text Mining: Nonlinear Dynamics

    Science.gov (United States)

    2004-02-01

    NOVOSIBIRSK NUCL PHYS INST SIBERIA 7 GREBOGI--C UNIV MARYLAND USA 6 MANDEL--P UNIV LIBRE BRUXELLES BELGIUM 6 iv SCOTT--SK UNIV LEEDS ENGLAND 6 STOOP--R UNIV...DM AM GORKII STATE UNIVERSITY UKRAINE 8 SHEPELYANSKY--DL NOVOSIBIRSK NUCL PHYS INST SIBERIA 7 GREBOGI--C UNIV MARYLAND USA 6 MANDEL--P UNIV LIBRE...ATMOSPHERE 10 ENVIRONMENT 92 Nonlinear Dynamics Text Mining Appendices Page 76 BIODIVERSITY 10 ENVIRONMENT 102 CALIFORNIA 10 ENVIRONMENT 112 ECOLOGY 10

  4. Science and Technology Text Mining: Cross-Disciplinary Innovation

    Science.gov (United States)

    2003-07-14

    1 SCIENCE AND TECHNOLOGY TEXT MINING : CROSS-DISCIPLINARY INNOVATION BY DR. RONALD N. KOSTOFF OFFICE OF NAVAL RESEARCH ARLINGTON, VA 22217 PHONE: 703...696-4198 FAX: 703-696-4274 INTERNET: kostofr@onr.navy.mil KEYWORDS: Innovation; text mining ; literature-based discovery; clustering; workshops; cross...TO) xx-xx-1999 to xx-xx-2003 4. TITLE AND SUBTITLE SCIENCE AND TECHNOLOGY TEXT MINING CROSS-DISCIPLINARY INNOVATION Unclassified 5a. CONTRACT NUMBER

  5. Text Mining.

    Science.gov (United States)

    Trybula, Walter J.

    1999-01-01

    Reviews the state of research in text mining, focusing on newer developments. The intent is to describe the disparate investigations currently included under the term text mining and provide a cohesive structure for these efforts. A summary of research identifies key organizations responsible for pushing the development of text mining. A section…

  6. Information Retrieval and Text Mining Technologies for Chemistry.

    Science.gov (United States)

    Krallinger, Martin; Rabal, Obdulia; Lourenço, Anália; Oyarzabal, Julen; Valencia, Alfonso

    2017-06-28

    Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

  7. Using Text Mining to Uncover Students' Technology-Related Problems in Live Video Streaming

    Science.gov (United States)

    Abdous, M'hammed; He, Wu

    2011-01-01

    Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…

  8. Contextual Text Mining

    Science.gov (United States)

    Mei, Qiaozhu

    2009-01-01

    With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

  9. Text mining for systems biology.

    Science.gov (United States)

    Fluck, Juliane; Hofmann-Apitius, Martin

    2014-02-01

    Scientific communication in biomedicine is, by and large, still text based. Text mining technologies for the automated extraction of useful biomedical information from unstructured text that can be directly used for systems biology modelling have been substantially improved over the past few years. In this review, we underline the importance of named entity recognition and relationship extraction as fundamental approaches that are relevant to systems biology. Furthermore, we emphasize the role of publicly organized scientific benchmarking challenges that reflect the current status of text-mining technology and are important in moving the entire field forward. Given further interdisciplinary development of systems biology-orientated ontologies and training corpora, we expect a steadily increasing impact of text-mining technology on systems biology in the future. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. A Customizable Text Classifier for Text Mining

    Directory of Open Access Journals (Sweden)

    Yun-liang Zhang

    2007-12-01

    Full Text Available Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically or be adjusted by user. The user can also control the number of domains chosen and decide the standard with which to choose the texts based on demand and abundance of materials. The performance of the classifier varies with the user's choice.

  11. Working with text tools, techniques and approaches for text mining

    CERN Document Server

    Tourte, Gregory J L

    2016-01-01

    Text mining tools and technologies have long been a part of the repository world, where they have been applied to a variety of purposes, from pragmatic aims to support tools. Research areas as diverse as biology, chemistry, sociology and criminology have seen effective use made of text mining technologies. Working With Text collects a subset of the best contributions from the 'Working with text: Tools, techniques and approaches for text mining' workshop, alongside contributions from experts in the area. Text mining tools and technologies in support of academic research include supporting research on the basis of a large body of documents, facilitating access to and reuse of extant work, and bridging between the formal academic world and areas such as traditional and social media. Jisc have funded a number of projects, including NaCTem (the National Centre for Text Mining) and the ResDis programme. Contents are developed from workshop submissions and invited contributions, including: Legal considerations in te...

  12. Text Mining Applications and Theory

    CERN Document Server

    Berry, Michael W

    2010-01-01

    Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.  The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning

  13. Psychologically Motivated Text Mining

    OpenAIRE

    Shutova, Ekaterina; Lichtenstein, Patricia

    2016-01-01

    Natural language processing techniques are increasingly applied to identify social trends and predict behavior based on large text collections. Existing methods typically rely on surface lexical and syntactic information. Yet, research in psychology shows that patterns of human conceptualisation, such as metaphorical framing, are reliable predictors of human expectations and decisions. In this paper, we present a method to learn patterns of metaphorical framing from large text collections, us...

  14. Metadata extraction using text mining.

    Science.gov (United States)

    Seth, Shivani; Rüping, Stefan; Wrobel, Stefan

    2009-01-01

    Grid technologies have proven to be very successful in the area of eScience, and healthcare in particular, because they allow to easily combine proven solutions for data querying, integration, and analysis into a secure, scalable framework. In order to integrate the services that implement these solutions into a given Grid architecture, some metadata is required, for example information about the low-level access to these services, security information, and some documentation for the user. In this paper, we investigate how relevant metadata can be extracted from a semi-structured textual documentation of the algorithm that is underlying the service, by the use of text mining methods. In particular, we investigate the semi-automatic conversion of functions of the statistical environment R into Grid services as implemented by the GridR tool by the generation of appropriate metadata.

  15. Biomarker Identification Using Text Mining

    Directory of Open Access Journals (Sweden)

    Hui Li

    2012-01-01

    Full Text Available Identifying molecular biomarkers has become one of the important tasks for scientists to assess the different phenotypic states of cells or organisms correlated to the genotypes of diseases from large-scale biological data. In this paper, we proposed a text-mining-based method to discover biomarkers from PubMed. First, we construct a database based on a dictionary, and then we used a finite state machine to identify the biomarkers. Our method of text mining provides a highly reliable approach to discover the biomarkers in the PubMed database.

  16. SIAM 2007 Text Mining Competition dataset

    Data.gov (United States)

    National Aeronautics and Space Administration — Subject Area: Text Mining Description: This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining...

  17. Text mining resources for the life sciences

    Science.gov (United States)

    Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia

    2016-01-01

    Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability. PMID:27888231

  18. Text mining resources for the life sciences.

    Science.gov (United States)

    Przybyła, Piotr; Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia

    2016-01-01

    Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability. © The Author(s) 2016. Published by Oxford University Press.

  19. GPU-Accelerated Text Mining

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Mueller, Frank [North Carolina State University; Zhang, Yongpeng [ORNL; Potok, Thomas E [ORNL

    2009-01-01

    Accelerating hardware devices represent a novel promise for improving the performance for many problem domains but it is not clear for which domains what accelerators are suitable. While there is no room in general-purpose processor design to significantly increase the processor frequency, developers are instead resorting to multi-core chips duplicating conventional computing capabilities on a single die. Yet, accelerators offer more radical designs with a much higher level of parallelism and novel programming environments. This present work assesses the viability of text mining on CUDA. Text mining is one of the key concepts that has become prominent as an effective means to index the Internet, but its applications range beyond this scope and extend to providing document similarity metrics, the subject of this work. We have developed and optimized text search algorithms for GPUs to exploit their potential for massive data processing. We discuss the algorithmic challenges of parallelization for text search problems on GPUs and demonstrate the potential of these devices in experiments by reporting significant speedups. Our study may be one of the first to assess more complex text search problems for suitability for GPU devices, and it may also be one of the first to exploit and report on atomic instruction usage that have recently become available in NVIDIA devices.

  20. Text mining by Tsallis entropy

    Science.gov (United States)

    Jamaati, Maryam; Mehri, Ali

    2018-01-01

    Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks the terms' relevance to document subject, taking advantage of their spatial correlation length. We apply this statistical concept as a new powerful word ranking metric in order to extract keywords of a single document. We carry out an experimental evaluation, which shows capability of the presented method in keyword extraction. We find that, Tsallis entropy has reliable word ranking performance, at the same level of the best previous ranking methods.

  1. Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

    Directory of Open Access Journals (Sweden)

    Qingliang Chang

    2014-01-01

    Full Text Available Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application.

  2. Text Mining for Protein Docking.

    Directory of Open Access Journals (Sweden)

    Varsha D Badal

    2015-12-01

    Full Text Available The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking. Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu. The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound

  3. Text Mining for Protein Docking.

    Science.gov (United States)

    Badal, Varsha D; Kundrotas, Petras J; Vakser, Ilya A

    2015-12-01

    The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set

  4. Knowledge discovery data and text mining

    CERN Document Server

    Olmer, Petr

    2008-01-01

    Data mining and text mining refer to techniques, models, algorithms, and processes for knowledge discovery and extraction. Basic de nitions are given together with the description of a standard data mining process. Common models and algorithms are presented. Attention is given to text clustering, how to convert unstructured text to structured data (vectors), and how to compute their importance and position within clusters.

  5. Text mining from ontology learning to automated text processing applications

    CERN Document Server

    Biemann, Chris

    2014-01-01

    This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects

  6. A STUDY OF TEXT MINING METHODS, APPLICATIONS,AND TECHNIQUES

    OpenAIRE

    R. Rajamani*1 & S. Saranya2

    2017-01-01

    Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mining, it is also called knowledge discovery in text (KDT) or knowledge of intelligent text analysis. T...

  7. Text Association Analysis and Ambiguity in Text Mining

    Science.gov (United States)

    Bhonde, S. B.; Paikrao, R. L.; Rahane, K. U.

    2010-11-01

    Text Mining is the process of analyzing a semantically rich document or set of documents to understand the content and meaning of the information they contain. The research in Text Mining will enhance human's ability to process massive quantities of information, and it has high commercial values. Firstly, the paper discusses the introduction of TM its definition and then gives an overview of the process of text mining and the applications. Up to now, not much research in text mining especially in concept/entity extraction has focused on the ambiguity problem. This paper addresses ambiguity issues in natural language texts, and presents a new technique for resolving ambiguity problem in extracting concept/entity from texts. In the end, it shows the importance of TM in knowledge discovery and highlights the up-coming challenges of document mining and the opportunities it offers.

  8. Methods for Mining and Summarizing Text Conversations

    CERN Document Server

    Carenini, Giuseppe; Murray, Gabriel

    2011-01-01

    Due to the Internet Revolution, human conversational data -- in written forms -- are accumulating at a phenomenal rate. At the same time, improvements in speech technology enable many spoken conversations to be transcribed. Individuals and organizations engage in email exchanges, face-to-face meetings, blogging, texting and other social media activities. The advances in natural language processing provide ample opportunities for these "informal documents" to be analyzed and mined, thus creating numerous new and valuable applications. This book presents a set of computational methods

  9. Monitoring interaction and collective text production through text mining

    Directory of Open Access Journals (Sweden)

    Macedo, Alexandra Lorandi

    2014-04-01

    Full Text Available This article presents the Concepts Network tool, developed using text mining technology. The main objective of this tool is to extract and relate terms of greatest incidence from a text and exhibit the results in the form of a graph. The Network was implemented in the Collective Text Editor (CTE which is an online tool that allows the production of texts in synchronized or non-synchronized forms. This article describes the application of the Network both in texts produced collectively and texts produced in a forum. The purpose of the tool is to offer support to the teacher in managing the high volume of data generated in the process of interaction amongst students and in the construction of the text. Specifically, the aim is to facilitate the teacher’s job by allowing him/her to process data in a shorter time than is currently demanded. The results suggest that the Concepts Network can aid the teacher, as it provides indicators of the quality of the text produced. Moreover, messages posted in forums can be analyzed without their content necessarily having to be pre-read.

  10. SparkText: Biomedical Text Mining on Big Data Framework.

    Directory of Open Access Journals (Sweden)

    Zhan Ye

    Full Text Available Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM, and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes.This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

  11. SparkText: Biomedical Text Mining on Big Data Framework.

    Science.gov (United States)

    Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M

    Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

  12. SparkText: Biomedical Text Mining on Big Data Framework

    Science.gov (United States)

    He, Karen Y.; Wang, Kai

    2016-01-01

    Background Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. PMID:27685652

  13. Cultural text mining: using text mining to map the emergence of transnational reference cultures in public media repositories

    NARCIS (Netherlands)

    Pieters, Toine; Verheul, Jaap

    2014-01-01

    This paper discusses the research project Translantis, which uses innovative technologies for cultural text mining to analyze large repositories of digitized public media, such as newspapers and journals.1 The Translantis research team uses and develops the text mining tool Texcavator, which is

  14. Anomaly Detection with Text Mining

    Data.gov (United States)

    National Aeronautics and Space Administration — Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. The...

  15. Text mining for the biocuration workflow.

    Science.gov (United States)

    Hirschman, Lynette; Burns, Gully A P C; Krallinger, Martin; Arighi, Cecilia; Cohen, K Bretonnel; Valencia, Alfonso; Wu, Cathy H; Chatr-Aryamontri, Andrew; Dowell, Karen G; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G

    2012-01-01

    Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

  16. Text Mining the Biomedical Literature

    Science.gov (United States)

    2007-11-05

    LECTURE NOTES IN COMPUTER SCIENCE Gelbukh, A; Sidorov, G; Guzman -Arenas, A. 1999. Use of a weighted topic hierarchy for document classification...matrix decomposition. ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 26 (3): 415-435. Kongovi, M; Guzman , JC; Dasigi, V. 2002. Text categorization: An...RECOGNITION, SPEECH AND IMAGE ANALYSIS 2905: 596-603. LECTURE NOTES IN COMPUTER SCIENCE Porter, AL; Kongthon, A; Lui , JC. 2002. Research profiling

  17. Frontiers of biomedical text mining: current progress

    Science.gov (United States)

    Zweigenbaum, Pierre; Demner-Fushman, Dina; Yu, Hong; Cohen, Kevin B.

    2008-01-01

    It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year. PMID:17977867

  18. Chapter 16: text mining for translational bioinformatics.

    Science.gov (United States)

    Cohen, K Bretonnel; Hunter, Lawrence E

    2013-04-01

    Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

  19. Text mining for the biocuration workflow

    Science.gov (United States)

    Hirschman, Lynette; Burns, Gully A. P. C; Krallinger, Martin; Arighi, Cecilia; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr-Aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G.

    2012-01-01

    Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community. PMID:22513129

  20. Text mining for biology--the way forward

    DEFF Research Database (Denmark)

    Altman, Russ B; Bergman, Casey M; Blake, Judith

    2008-01-01

    This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify...... several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger...

  1. Text mining and visualization using VOSviewer

    CERN Document Server

    van Eck, Nees Jan

    2011-01-01

    VOSviewer is a computer program for creating, visualizing, and exploring bibliometric maps of science. In this report, the new text mining functionality of VOSviewer is presented. A number of examples are given of applications in which VOSviewer is used for analyzing large amounts of text data.

  2. Text mining patents for biomedical knowledge.

    Science.gov (United States)

    Rodriguez-Esteban, Raul; Bundschus, Markus

    2016-06-01

    Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Text mining with R a tidy approach

    CERN Document Server

    Silge, Julia

    2017-01-01

    Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document's most important terms with frequency measurements E...

  4. Benchmarking infrastructure for mutation text mining

    Science.gov (United States)

    2014-01-01

    Background Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. Results We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. Conclusion We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption. PMID:24568600

  5. CONAN : Text Mining in the Biomedical Domain

    NARCIS (Netherlands)

    Malik, R.

    2006-01-01

    This thesis is about Text Mining. Extracting important information from literature. In the last years, the number of biomedical articles and journals is growing exponentially. Scientists might not find the information they want because of the large number of publications. Therefore a system was

  6. Text Mining applied to Molecular Biology

    NARCIS (Netherlands)

    R. Jelier (Rob)

    2008-01-01

    textabstractThis thesis describes the development of text-mining algorithms for molecular biology, in particular for DNA microarray data analysis. Concept profiles were introduced, which characterize the context in which a gene is mentioned in literature, to retrieve functional associations

  7. Citation Mining: Integrating Text Mining and Bibliometrics for Research User Profiling.

    Science.gov (United States)

    Kostoff, Ronald N.; del Rio, J. Antonio; Humenik, James A.; Garcia, Esther Ofilia; Ramirez, Ana Maria

    2001-01-01

    Discusses the importance of identifying the users and impact of research, and describes an approach for identifying the pathways through which research can impact other research, technology development, and applications. Describes a study that used citation mining, an integration of citation bibliometrics and text mining, on articles from the…

  8. Text Mining for Drug–Drug Interaction

    Science.gov (United States)

    Wu, Heng-Yi; Chiang, Chien-Wei; Li, Lang

    2015-01-01

    In order to understand the mechanisms of drug–drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases. To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis. The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions. PMID:24788261

  9. Text mining for drug-drug interaction.

    Science.gov (United States)

    Wu, Heng-Yi; Chiang, Chien-Wei; Li, Lang

    2014-01-01

    In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

  10. Analysing Customer Opinions with Text Mining Algorithms

    Science.gov (United States)

    Consoli, Domenico

    2009-08-01

    Knowing what the customer thinks of a particular product/service helps top management to introduce improvements in processes and products, thus differentiating the company from their competitors and gain competitive advantages. The customers, with their preferences, determine the success or failure of a company. In order to know opinions of the customers we can use technologies available from the web 2.0 (blog, wiki, forums, chat, social networking, social commerce). From these web sites, useful information must be extracted, for strategic purposes, using techniques of sentiment analysis or opinion mining.

  11. The Role of Text Mining in Export Control

    Energy Technology Data Exchange (ETDEWEB)

    Tae, Jae-woong; Son, Choul-woong; Shin, Dong-hoon [Korea Institute of Nuclear Nonproliferation and Control, Daejeon (Korea, Republic of)

    2015-10-15

    Korean government provides classification services to exporters. It is simple to copy technology such as documents and drawings. Moreover, it is also easy that new technology derived from the existing technology. The diversity of technology makes classification difficult because the boundary between strategic and nonstrategic technology is unclear and ambiguous. Reviewers should consider previous classification cases enough. However, the increase of the classification cases prevent consistent classifications. This made another innovative and effective approaches necessary. IXCRS (Intelligent Export Control Review System) is proposed to coincide with demands. IXCRS consists of and expert system, a semantic searching system, a full text retrieval system, and image retrieval system and a document retrieval system. It is the aim of the present paper to observe the document retrieval system based on text mining and to discuss how to utilize the system. This study has demonstrated how text mining technique can be applied to export control. The document retrieval system supports reviewers to treat previous classification cases effectively. Especially, it is highly probable that similarity data will contribute to specify classification criterion. However, an analysis of the system showed a number of problems that remain to be explored such as a multilanguage problem and an inclusion relationship problem. Further research should be directed to solve problems and to apply more data mining techniques so that the system should be used as one of useful tools for export control.

  12. Text Mining the History of Medicine.

    Directory of Open Access Journals (Sweden)

    Paul Thompson

    Full Text Available Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc., synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.. TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research

  13. Text Mining the History of Medicine

    Science.gov (United States)

    Thompson, Paul; Batista-Navarro, Riza Theresa; Kontonatsios, Georgios; Carter, Jacob; Toon, Elizabeth; McNaught, John; Timmermann, Carsten; Worboys, Michael; Ananiadou, Sophia

    2016-01-01

    Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while

  14. Text Mining the History of Medicine.

    Science.gov (United States)

    Thompson, Paul; Batista-Navarro, Riza Theresa; Kontonatsios, Georgios; Carter, Jacob; Toon, Elizabeth; McNaught, John; Timmermann, Carsten; Worboys, Michael; Ananiadou, Sophia

    2016-01-01

    Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while

  15. Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

    Science.gov (United States)

    Chang, Qingliang; Zhou, Huaqiang; Bai, Jianbiao

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application. PMID:25258737

  16. Implementation of paste backfill mining technology in Chinese coal mines.

    Science.gov (United States)

    Chang, Qingliang; Chen, Jianhang; Zhou, Huaqiang; Bai, Jianbiao

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application.

  17. Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

    OpenAIRE

    Qingliang Chang; Jianhang Chen; Huaqiang Zhou; Jianbiao Bai

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology a...

  18. Text Mining Metal-Organic Framework Papers.

    Science.gov (United States)

    Park, Sanghoon; Kim, Baekjun; Choi, Sihoon; Boyd, Peter G; Smit, Berend; Kim, Jihan

    2018-02-26

    We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal-organic frameworks (MOFs) using manuscript html files as inputs. The algorithm searches for common units (e.g., m 2 /g, cm 3 /g) associated with these two quantities to facilitate the search. From the sample set data of over 200 MOFs, the algorithm managed to identify 90% and 88.8% of the correct surface area and pore volume values. Further application to a test set of randomly chosen MOF html files yielded 73.2% and 85.1% accuracies for the two respective quantities. Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs (e.g., 1a) that made it difficult identify its real name. These types of tools will become useful when it comes to discovering structure-property relationships among MOFs as well as collecting a large set of data for references.

  19. Text Mining in Biomedical Domain with Emphasis on Document Clustering

    Science.gov (United States)

    2017-01-01

    Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

  20. Text Mining in Biomedical Domain with Emphasis on Document Clustering.

    Science.gov (United States)

    Renganathan, Vinaitheerthan

    2017-07-01

    With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

  1. DATA MINING TECHNOLOGIES

    OpenAIRE

    Titrade Cristina-Maria

    2010-01-01

    Knowledge discovery and data mining software (Knowledge Discovery and Data Mining - KDD) as an interdisciplinary field emersion have been in rapid growth to merge databases, statistics, industries closely related to the desire to extract valuable information and knowledge in a volume as possible.There is a difference in understanding of "knowledge discovery" and "data mining." Discovery information (Knowledge Discovery) in the database is a process to identify patterns / templates of valid da...

  2. Negotiating a Text Mining License for Faculty Researchers

    Directory of Open Access Journals (Sweden)

    Leslie A. Williams

    2014-09-01

    Full Text Available This case study examines strategies used to leverage the library’s existing journal licenses to obtain a large collection of full-text journal articles in extensible markup language (XML format; the right to text mine the collection; and the right to use the collection and the data mined from it for grant-funded research to develop biomedical natural language processing (BNLP tools. Researchers attempted to obtain content directly from PubMed Central (PMC. This attempt failed due to limits on use of content in PMC. Next researchers and their library liaison attempted to obtain content from contacts in the technical divisions of the publishing industry. This resulted in an incomplete research data set. Then researchers, the library liaison, and the acquisitions librarian collaborated with the sales and technical staff of a major science, technology, engineering, and medical (STEM publisher to successfully create a method for obtaining XML content as an extension of the library’s typical acquisition process for electronic resources. Our experience led us to realize that text mining rights of full-text articles in XML format should routinely be included in the negotiation of the library’s licenses.

  3. Mining and Reclamation Technology Symposium

    Energy Technology Data Exchange (ETDEWEB)

    None Available

    1999-06-24

    The Mining and Reclamation Technology Symposium was commissioned by the Mountaintop Removal Mining/Valley Fill Environmental Impact Statement (EIS) Interagency Steering Committee as an educational forum for the members of the regulatory community who will participate in the development of the EIS. The Steering Committee sought a balanced audience to ensure the input to the regulatory community reflected the range of perspectives on this complicated and emotional issue. The focus of this symposium is on mining and reclamation technology alternatives, which is one of eleven topics scheduled for review to support development of the EIS. Others include hydrologic, environmental, ecological, and socio-economic issues.

  4. Text-mining analysis of mHealth research.

    Science.gov (United States)

    Ozaydin, Bunyamin; Zengul, Ferhat; Oner, Nurettin; Delen, Dursun

    2017-01-01

    In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging. The most recent developments in machine learning and text mining offer some potential solutions to address this challenge by allowing analyses of large volumes of texts through semi-automated processes. The objective of this study is to analyze the evolution of mHealth research by utilizing text-mining and natural language processing (NLP) analyses. The study sample included abstracts of 5,644 mHealth research articles, which were gathered from five academic search engines by using search terms such as mobile health, and mHealth. The analysis used the Text Explorer module of JMP Pro 13 and an iterative semi-automated process involving tokenizing, phrasing, and terming. After developing the document term matrix (DTM) analyses such as single value decomposition (SVD), topic, and hierarchical document clustering were performed, along with the topic-informed document clustering approach. The results were presented in the form of word-clouds and trend analyses. There were several major findings regarding research clusters and trends. First, our results confirmed time-dependent nature of terminology use in mHealth research. For example, in earlier versus recent years the use of terminology changed from "mobile phone" to "smartphone" and from "applications" to "apps". Second, ten clusters for mHealth research were identified including (I) Clinical Research on Lifestyle Management, (II) Community Health, (III) Literature Review, (IV) Medical Interventions

  5. Text mining in livestock animal science: introducing the potential of text mining to animal sciences.

    Science.gov (United States)

    Sahadevan, S; Hofmann-Apitius, M; Schellander, K; Tesfaye, D; Fluck, J; Friedrich, C M

    2012-10-01

    In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i) various biological databases and ii) scientific publications in the field. The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text. The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information. Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text. Named entity recognition (NER) is defined as the method of identifying named entities (names of real world objects; for example, gene/protein names, drugs, enzymes) in text. In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from

  6. Text mining meets workflow: linking U-Compare with Taverna

    Science.gov (United States)

    Kano, Yoshinobu; Dobson, Paul; Nakanishi, Mio; Tsujii, Jun'ichi; Ananiadou, Sophia

    2010-01-01

    Summary: Text mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run text mining workflows due to the lack of accessibility and interoperability of the text mining resources. The U-Compare system provides a wide range of bio text mining resources in a highly interoperable workflow environment where workflows can very easily be created, executed, evaluated and visualized without coding. We have linked U-Compare to Taverna, a generic workflow system, to expose text mining functionality to the bioinformatics community. Availability: http://u-compare.org/taverna.html, http://u-compare.org Contact: kano@is.s.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20709690

  7. Biomedical text mining and its applications in cancer research.

    Science.gov (United States)

    Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong

    2013-04-01

    Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.

  8. Text mining factor analysis (TFA) in green tea patent data

    Science.gov (United States)

    Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

    2017-03-01

    Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

  9. Text mining of web-based medical content

    CERN Document Server

    Neustein, Amy

    2014-01-01

    Text Mining of Web-Based Medical Content examines web mining for extracting useful information that can be used for treating and monitoring the healthcare of patients. This work provides methodological approaches to designing mapping tools that exploit data found in social media postings. Specific linguistic features of medical postings are analyzed vis-a-vis available data extraction tools for culling useful information.

  10. PathText: a text mining integrator for biological pathway visualizations

    Science.gov (United States)

    Kemper, Brian; Matsuzaki, Takuya; Matsuoka, Yukiko; Tsuruoka, Yoshimasa; Kitano, Hiroaki; Ananiadou, Sophia; Tsujii, Jun'ichi

    2010-01-01

    Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com. PMID:20529930

  11. Text mining in cancer gene and pathway prioritization.

    Science.gov (United States)

    Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

    2014-01-01

    Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

  12. Application of text mining in the biomedical domain.

    Science.gov (United States)

    Fleuren, Wilco W M; Alkema, Wynand

    2015-03-01

    In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Application of text mining for customer evaluations in commercial banking

    Science.gov (United States)

    Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

    2015-07-01

    Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

  14. Text mining for traditional Chinese medical knowledge discovery: a survey.

    Science.gov (United States)

    Zhou, Xuezhong; Peng, Yonghong; Liu, Baoyan

    2010-08-01

    Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions. Copyright 2010 Elsevier Inc. All rights reserved.

  15. Technological challenges for manganese nodule mining

    Digital Repository Service at National Institute of Oceanography (India)

    Sharma, R.

    The major technological challenges of deep-sea mining venture involve delineation of mine site and development of mining technology to bring out the minerals from extreme conditions (more than 5 km water depth, 0-3 degrees C temperature and 500 bars...

  16. Gene prioritization and clustering by multi-view text mining

    Directory of Open Access Journals (Sweden)

    De Moor Bart

    2010-01-01

    Full Text Available Abstract Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification.

  17. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

    Science.gov (United States)

    Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

    2016-01-01

    Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. PMID:28025348

  18. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

    Science.gov (United States)

    Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

    2016-01-01

    Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

  19. Gene prioritization and clustering by multi-view text mining.

    Science.gov (United States)

    Yu, Shi; Tranchevent, Leon-Charles; De Moor, Bart; Moreau, Yves

    2010-01-14

    Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification.

  20. OntoGene web services for biomedical text mining.

    Science.gov (United States)

    Rinaldi, Fabio; Clematide, Simon; Marques, Hernani; Ellendorff, Tilia; Romacker, Martin; Rodriguez-Esteban, Raul

    2014-01-01

    Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

  1. Supported eText: Assistive Technology through Text Transformations

    Science.gov (United States)

    Anderson-Inman, Lynne; Horney, Mark A.

    2007-01-01

    To gain meaningful access to the curriculum, students with reading difficulties must overcome substantial barriers imposed by the printed materials they are asked to read. Technology can assist students to overcome these challenges by enabling a shift from printed text to electronic text. By electronic text it means textual material read using a…

  2. Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

    Science.gov (United States)

    Bekhuis, Tanja

    2006-04-03

    Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.

  3. pubmed. mineR: An R package with text-mining algorithms to ...

    Indian Academy of Sciences (India)

    Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, ...

  4. Mining knowledge from text repositories using information extraction ...

    Indian Academy of Sciences (India)

    Computational Linguistics, Stroudsburg, PA, USA, pp 66–73. Rose S, Engel D, Cramer N and Cowley W 2010 Automatic keyword extraction from individual document,. Text mining: Application and theory, M W Berry and J Kogan (eds) John Willey & Sons Ltd 2010, pp 3–20. Sánchez D, Martín-Bautista M J and Blanco I 2008 ...

  5. Identifying child abuse through text mining and machine learning

    NARCIS (Netherlands)

    Amrit, Chintan; Paauw, Tim; Aly, Robin; Lavric, Miha

    2017-01-01

    In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on

  6. The Application of Text Mining in Business Research

    DEFF Research Database (Denmark)

    Preuss, Bjørn

    2017-01-01

    The aim of this paper is to present a methodological concept in business research that has the potential to become one of the most powerful methods in the upcoming years when it comes to research qualitative phenomena in business and society. It presents a selection of algorithms as well elaborat...... on potential use cases for a text mining based approach to qualitative data analysis....

  7. Using Text Mining to Characterize Online Discussion Facilitation

    Science.gov (United States)

    Ming, Norma; Baumer, Eric

    2011-01-01

    Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…

  8. Text Mining of Journal Articles for Sleep Disorder Terminologies.

    Directory of Open Access Journals (Sweden)

    Calvin Lam

    Full Text Available Research on publication trends in journal articles on sleep disorders (SDs and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings.SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms.MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms.Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

  9. Text Mining of Journal Articles for Sleep Disorder Terminologies.

    Science.gov (United States)

    Lam, Calvin; Lai, Fu-Chih; Wang, Chia-Hui; Lai, Mei-Hsin; Hsu, Nanly; Chung, Min-Huey

    2016-01-01

    Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms. MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms. Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

  10. Mining the Text: 34 Text Features that Can Ease or Obstruct Text Comprehension and Use

    Science.gov (United States)

    White, Sheida

    2012-01-01

    This article presents 34 characteristics of texts and tasks ("text features") that can make continuous (prose), noncontinuous (document), and quantitative texts easier or more difficult for adolescents and adults to comprehend and use. The text features were identified by examining the assessment tasks and associated texts in the national…

  11. Channels to mining industry and technology market

    OpenAIRE

    Mantila, K. (Kimmo)

    2013-01-01

    This research is a Master´s thesis that has made for Parker Hannifin Oy in the University of Oulu in Department of Process and Environmental engineering. Parker Hannifin Oy ordered a market research of the Finnish mining market. A subject was a limited research to the northern Fennoscandian and Greenland mining market but the main focus is in Finnish mining industry and technology market. The research produced new information about the mining market for the decision makers of Parker Hannifin ...

  12. Text mining improves prediction of protein functional sites.

    Directory of Open Access Journals (Sweden)

    Karin M Verspoor

    Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

  13. Deep Learning for text data mining: Solving spreadsheet data classification.

    OpenAIRE

    Kimashev, Aleksandr

    2017-01-01

    Master's thesis in Computer science This project developed for the Avito LOOPS company. Research goals was to investigate existing algorithms and implementations of Deep Learning, to understand their applicability to text mining, to design a solution that incorporates theoretical and practical aspects, to run classification experiments on different data sets so that the pros and cons of different techniques can be understood. Classification of the text was necessary for the spreadsheet co...

  14. Using Text Mining for Unsupervised Knowledge Extraction and Organization

    Directory of Open Access Journals (Sweden)

    REZENDE, S. O.

    2011-06-01

    Full Text Available The progress in digitally generated data aquisition and storage has allowed for a huge growth in information generated in organizations. Around 80% ofthose data are created in non structured format and a significant part of those are texts. Intelligent organization of those textual collection is a matter of interest for most organizations, for it speed up information search and retrieval. In this context, Text Mining can transform this great amount non structure text data un useful knowledge, that can even be innovative for those organizations. Using unsupervised methods for knowledge extraction and organization has received great attention in literature, because it does not require previous knowledge on the textual collections that are going to be explored. In this article we describe the main techniques and algorithms used for unsupervised knowledege extraction and organization from textual data. The most relevant works in literature are presented and discussed in each phase of the Text Mining process and some existing computational tools are suggested for each task at hand. At last, some examples and applications are present to show the use of Text Mining on real problems.

  15. Text Mining Improves Prediction of Protein Functional Sites

    Science.gov (United States)

    Cohn, Judith D.; Ravikumar, Komandur E.

    2012-01-01

    We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

  16. Science and Technology Text Mining: Electrochemical Power

    Science.gov (United States)

    2003-07-14

    dispersed, linio2 positive, inert, multinary alloy, hydrogen, cl-2, carbon fiber, glassy carbon, manganese dioxide, ion-selective, spinel, zeolite -modified...electrode; § hydrogen absorbing metals for NiMH anodes; § alloy powders for higher energy capacity and improved cycle life; § carbon coated silicon for...ELECTROCHEM SOC V139 300 (LI METAL-FREE RECHARGEABLE LIMN2O4/ CARBON CELLS) THACKERAY MM 1983 MATER RES BULL V18 358 (LITHIUM INSERTION INTO MANGANESE SPINELS

  17. Empirical advances with text mining of electronic health records.

    Science.gov (United States)

    Delespierre, T; Denormandie, P; Bar-Hen, A; Josseran, L

    2017-08-22

    Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives. Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs. By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and

  18. Text Mining to Support Gene Ontology Curation and Vice Versa.

    Science.gov (United States)

    Ruch, Patrick

    2017-01-01

    In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

  19. Aspects of Text Mining From Computational Semiotics to Systemic Functional Hypertexts

    Directory of Open Access Journals (Sweden)

    Alexander Mehler

    2001-05-01

    Full Text Available The significance of natural language texts as the prime information structure for the management and dissemination of knowledge in organisations is still increasing. Making relevant documents available depending on varying tasks in different contexts is of primary importance for any efficient task completion. Implementing this demand requires the content based processing of texts, which enables to reconstruct or, if necessary, to explore the relationship of task, context and document. Text mining is a technology that is suitable for solving problems of this kind. In the following, semiotic aspects of text mining are investigated. Based on the primary object of text mining - natural language lexis - the specific complexity of this class of signs is outlined and requirements for the implementation of text mining procedures are derived. This is done with reference to text linkage introduced as a special task in text mining. Text linkage refers to the exploration of implicit, content based relations of texts (and their annotation as typed links in corpora possibly organised as hypertexts. In this context, the term systemic functional hypertext is introduced, which distinguishes genre and register layers for the management of links in a poly-level hypertext system.

  20. Text mining and medicine: usefulness in respiratory diseases.

    Science.gov (United States)

    Piedra, David; Ferrer, Antoni; Gea, Joaquim

    2014-03-01

    It is increasingly common to have medical information in electronic format. This includes scientific articles as well as clinical management reviews, and even records from health institutions with patient data. However, traditional instruments, both individual and institutional, are of little use for selecting the most appropriate information in each case, either in the clinical or research field. So-called text or data «mining» enables this huge amount of information to be managed, extracting it from various sources using processing systems (filtration and curation), integrating it and permitting the generation of new knowledge. This review aims to provide an overview of text and data mining, and of the potential usefulness of this bioinformatic technique in the exercise of care in respiratory medicine and in research in the same field. Copyright © 2013 SEPAR. Published by Elsevier Espana. All rights reserved.

  1. Hot complaint intelligent classification based on text mining

    Directory of Open Access Journals (Sweden)

    XIA Haifeng

    2013-10-01

    Full Text Available The complaint recognizer system plays an important role in making sure the correct classification of the hot complaint,improving the service quantity of telecommunications industry.The customers’ complaint in telecommunications industry has its special particularity which should be done in limited time,which cause the error in classification of hot complaint.The paper presents a model of complaint hot intelligent classification based on text mining,which can classify the hot complaint in the correct level of the complaint navigation.The examples show that the model can be efficient to classify the text of the complaint.

  2. Text mining a self-report back-translation.

    Science.gov (United States)

    Blanch, Angel; Aluja, Anton

    2016-06-01

    There are several recommendations about the routine to undertake when back translating self-report instruments in cross-cultural research. However, text mining methods have been generally ignored within this field. This work describes a text mining innovative application useful to adapt a personality questionnaire to 12 different languages. The method is divided in 3 different stages, a descriptive analysis of the available back-translated instrument versions, a dissimilarity assessment between the source language instrument and the 12 back-translations, and an item assessment of item meaning equivalence. The suggested method contributes to improve the back-translation process of self-report instruments for cross-cultural research in 2 significant intertwined ways. First, it defines a systematic approach to the back translation issue, allowing for a more orderly and informed evaluation concerning the equivalence of different versions of the same instrument in different languages. Second, it provides more accurate instrument back-translations, which has direct implications for the reliability and validity of the instrument's test scores when used in different cultures/languages. In addition, this procedure can be extended to the back-translation of self-reports measuring psychological constructs in clinical assessment. Future research works could refine the suggested methodology and use additional available text mining tools. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  3. A Text-Mining Framework for Supporting Systematic Reviews.

    Science.gov (United States)

    Li, Dingcheng; Wang, Zhen; Wang, Liwei; Sohn, Sunghwan; Shen, Feichen; Murad, Mohammad Hassan; Liu, Hongfang

    2016-11-01

    Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.

  4. OSCAR4: a flexible architecture for chemical text-mining

    Directory of Open Access Journals (Sweden)

    Jessop David M

    2011-10-01

    Full Text Available Abstract The Open-Source Chemistry Analysis Routines (OSCAR software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.

  5. Energy efficient technologies for the mining industry

    Energy Technology Data Exchange (ETDEWEB)

    Klein, B.; Bamber, A.; Weatherwax, T.; Dozdiak, J.; Nadolski, S.; Roufail, R.; Parry, J.; Roufail, R.; Tong, L.; Hall, R. [British Columbia Univ., Vancouver, BC (Canada). Centre for Environmental Research in Minerals, Metals and Materials, Norman B. Keevil Inst. of Mining Engineering

    2010-07-01

    Mining in British Columbia is the second largest industrial electricity consumer. This presentation highlighted methods to help the mining industry reduce their energy requirements by limiting waste and improving efficiency. The measures are aimed at optimizing energy-use and efficiency in mining and processing and identifying opportunities and methods of improving this efficiency. Energy conservation in comminution and beneficiation is a primary focus of research activities at the University of British Columbia (UBC). The objective is to reduce energy usage in metal mines by 20 per cent overall. Open pit copper, gold and molybdenum mines are being targeted. Projects underway at UBC were outlined, with particular reference to energy usage, recovery and alternative energy sources; preconcentration; reducing energy usage from comminution in sorting, high pressure grinding rolls and high speed stirred mills; Hydromet; other energy efficient technologies such as control and flotation; and carbon dioxide sequestration. Studies were conducted at various mining facilities, including mines in Sudbury, Ontario. tabs., figs.

  6. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  7. Spectral signature verification using statistical analysis and text mining

    Science.gov (United States)

    DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

    2016-05-01

    In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is

  8. Identifying topics of interest of Mendeley users using the text mining and overlay visualization functionality of VOS viewer. 20th International Conference in Science & Technology Indicators, 2-4, September, 2015, Lugano, Switzerland

    NARCIS (Netherlands)

    Zahedi, Z.; Van, Eck N.J.P.

    2015-01-01

    This paper presents the results of a study in which we have analysed the topics of interest of Mendeley users (i.e. Students, PhDs, Post Docs, Researchers, Professors, Librarians, Lecturers & other Professionals) using text mining and visualization techniques. Beside analyzing topics of interest of

  9. Building a glaucoma interaction network using a text mining approach.

    Science.gov (United States)

    Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

    2016-01-01

    The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

  10. Unsupervised text mining for assessing and augmenting GWAS results.

    Science.gov (United States)

    Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

    2016-04-01

    Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Mining Protein Interactions from Text Using Convolution Kernels

    Science.gov (United States)

    Narayanan, Ramanathan; Misra, Sanchit; Lin, Simon; Choudhary, Alok

    As the sizes of biomedical literature databases increase, there is an urgent need to develop intelligent systems that automatically discover Protein-Protein interactions from text. Despite resource-intensive efforts to create manually curated interaction databases, the sheer volume of biological literature databases makes it impossible to achieve significant coverage. In this paper, we describe a scalable hierarchical Support Vector Machine(SVM) based framework to efficiently mine protein interactions with high precision. In addition, we describe a convolution tree-vector kernel based on syntactic similarity of natural language text to further enhance the mining process. By using the inherent syntactic similarity of interaction phrases as a kernel method, we are able to significantly improve the classification quality. Our hierarchical framework allows us to reduce the search space dramatically with each stage, while sustaining a high level of accuracy. We test our framework on a corpus of over 10000 manually annotated phrases gathered from various sources. The convolution kernel technique identifies sentences describing interactions with a precision of 95% and a recall of 92%, yielding significant improvements over previous machine learning techniques.

  12. Mining Sequential Update Summarization with Hierarchical Text Analysis

    Directory of Open Access Journals (Sweden)

    Chunyun Zhang

    2016-01-01

    Full Text Available The outbreak of unexpected news events such as large human accident or natural disaster brings about a new information access problem where traditional approaches fail. Mostly, news of these events shows characteristics that are early sparse and later redundant. Hence, it is very important to get updates and provide individuals with timely and important information of these incidents during their development, especially when being applied in wireless and mobile Internet of Things (IoT. In this paper, we define the problem of sequential update summarization extraction and present a new hierarchical update mining system which can broadcast with useful, new, and timely sentence-length updates about a developing event. The new system proposes a novel method, which incorporates techniques from topic-level and sentence-level summarization. To evaluate the performance of the proposed system, we apply it to the task of sequential update summarization of temporal summarization (TS track at Text Retrieval Conference (TREC 2013 to compute four measurements of the update mining system: the expected gain, expected latency gain, comprehensiveness, and latency comprehensiveness. Experimental results show that our proposed method has good performance.

  13. Practical text mining and statistical analysis for non-structured text data applications

    CERN Document Server

    Miner, Gary; Hill, Thomas; Nisbet, Robert; Delen, Dursun

    2012-01-01

    The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase d

  14. CrossRef text and data mining services

    Directory of Open Access Journals (Sweden)

    Rachael Lammey

    2015-02-01

    Full Text Available CrossRef is an association of scholarly publishers that develops shared infrastructure to support more effective scholarly communications. It is a registration agency for the digital object identifier (DOI, and has built additional services for CrossRef members around the DOI and the bibliographic metadata that publishers deposit in order to register DOIs for their publications. Among these services are CrossCheck, powered by iThenticate, which helps publishers screen for plagiarism in submitted manuscripts and FundRef, which gives publishers standard way to report funding sources for published scholarly research. To add to these services, Cross-Ref launched CrossRef text and data mining services in May 2014. This article will explain the thinking behind CrossRef launching this new service, what it offers to publishers and researchers alike, how publishers can participate in it, and the uptake of the service so far.

  15. EnvMine: A text-mining system for the automatic extraction of contextual information

    Directory of Open Access Journals (Sweden)

    de Lorenzo Victor

    2010-06-01

    Full Text Available Abstract Background For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles. So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations from textual sources of any kind. Results EnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings. Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude, thus allowing the calculation of distance between the individual locations. Conclusion EnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical

  16. Data mining of text as a tool in authorship attribution

    Science.gov (United States)

    Visa, Ari J. E.; Toivonen, Jarmo; Autio, Sami; Maekinen, Jarno; Back, Barbro; Vanharanta, Hannu

    2001-03-01

    It is common that text documents are characterized and classified by keywords that the authors use to give them. Visa et al. have developed a new methodology based on prototype matching. The prototype is an interesting document or a part of an extracted, interesting text. This prototype is matched with the document database of the monitored document flow. The new methodology is capable of extracting the meaning of the document in a certain degree. Our claim is that the new methodology is also capable of authenticating the authorship. To verify this claim two tests were designed. The test hypothesis was that the words and the word order in the sentences could authenticate the author. In the first test three authors were selected. The selected authors were William Shakespeare, Edgar Allan Poe, and George Bernard Shaw. Three texts from each author were examined. Every text was one by one used as a prototype. The two nearest matches with the prototype were noted. The second test uses the Reuters-21578 financial news database. A group of 25 short financial news reports from five different authors are examined. Our new methodology and the interesting results from the two tests are reported in this paper. In the first test, for Shakespeare and for Poe all cases were successful. For Shaw one text was confused with Poe. In the second test the Reuters-21578 financial news were identified by the author relatively well. The resolution is that our text mining methodology seems to be capable of authorship attribution.

  17. EnvMine: a text-mining system for the automatic extraction of contextual information.

    Science.gov (United States)

    Tamames, Javier; de Lorenzo, Victor

    2010-06-01

    For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind. EnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved) of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings.Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude), thus allowing the calculation of distance between the individual locations. EnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical variables of sampling sites, thus facilitating the performance

  18. Text-mining-assisted biocuration workflows in Argo

    Science.gov (United States)

    Rak, Rafal; Batista-Navarro, Riza Theresa; Rowley, Andrew; Carter, Jacob; Ananiadou, Sophia

    2014-01-01

    Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk PMID

  19. Sentiment analysis of Arabic tweets using text mining techniques

    Science.gov (United States)

    Al-Horaibi, Lamia; Khan, Muhammad Badruddin

    2016-07-01

    Sentiment analysis has become a flourishing field of text mining and natural language processing. Sentiment analysis aims to determine whether the text is written to express positive, negative, or neutral emotions about a certain domain. Most sentiment analysis researchers focus on English texts, with very limited resources available for other complex languages, such as Arabic. In this study, the target was to develop an initial model that performs satisfactorily and measures Arabic Twitter sentiment by using machine learning approach, Naïve Bayes and Decision Tree for classification algorithms. The datasets used contains more than 2,000 Arabic tweets collected from Twitter. We performed several experiments to check the performance of the two algorithms classifiers using different combinations of text-processing functions. We found that available facilities for Arabic text processing need to be made from scratch or improved to develop accurate classifiers. The small functionalities developed by us in a Python language environment helped improve the results and proved that sentiment analysis in the Arabic domain needs lot of work on the lexicon side.

  20. Context-sensitive keyword selection using text data mining

    Science.gov (United States)

    Li, Sai-Ming; Seereeram, Sanjeev; Mehra, Raman K.; Miles, Chris

    2002-03-01

    Most information retrieval systems rely on the user to provide a set of keywords that the retrieved documents should contain. However, when the objective is to search for documents that is similar to a given document, the system has to choose the keywords from that document first. Automatic selection of keywords is not a trivial task as one word may be a keyword in one context but a very common word in others, and require significant domain specific knowledge. In this paper we describe a method for choosing keywords from a document within a given corpus automatically using text data-mining technique. The key idea is to score the words within the document based on the clustering result of the entire corpus. We applied the scheme to a Software Trouble Report (STR) corpus and obtained highly relevant keywords and search result.

  1. Text Mining approaches for automated literature knowledge extraction and representation.

    Science.gov (United States)

    Nuzzo, Angelo; Mulas, Francesca; Gabetta, Matteo; Arbustini, Eloisa; Zupan, Blaz; Larizza, Cristiana; Bellazzi, Riccardo

    2010-01-01

    Due to the overwhelming volume of published scientific papers, information tools for automated literature analysis are essential to support current biomedical research. We have developed a knowledge extraction tool to help researcher in discovering useful information which can support their reasoning process. The tool is composed of a search engine based on Text Mining and Natural Language Processing techniques, and an analysis module which process the search results in order to build annotation similarity networks. We tested our approach on the available knowledge about the genetic mechanism of cardiac diseases, where the target is to find both known and possible hypothetical relations between specific candidate genes and the trait of interest. We show that the system i) is able to effectively retrieve medical concepts and genes and ii) plays a relevant role assisting researchers in the formulation and evaluation of novel literature-based hypotheses.

  2. Protein-protein interaction predictions using text mining methods.

    Science.gov (United States)

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

    2015-03-01

    It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Aerosol sensing technologies in the mining industry

    Science.gov (United States)

    Janisko, Samuel J.; Noll, James D.; Cauda, Emanuele E.

    2011-06-01

    Recent health, safety and environmental regulations are causing an increased demand for monitoring of aerosols in the mining industry. Of particular concern are airborne concentrations of combustible and toxic rock dusts as well as particulate matter generated from diesel engines in underground mines. In response, the National Institute for Occupational Safety and Health (NIOSH) has been evaluating a number of real time sensing technologies for potential use in underground mines. In particular, extensive evaluation has been done on filter-based light extinction using elemental carbon (EC) as a surrogate measurement of total diesel particulate matter (DPM) mass concentration as well as mechanical tapered element oscillating microbalance (TEOM) technology for measurement of both DPM and rock dust mass concentrations. Although these technologies are promising in their ability to accurately measure mine aerosols for their respective applications, there are opportunities for design improvements or alternative technologies that may significantly enhance the monitoring of mine aerosols. Such alterations can lead to increases in sensitivity or a reduction in the size and cost of these devices. This paper provides a brief overview of current practices and presents results of NIOSH research in this area. It concludes with a short discussion of future directions in mine aerosol sensing research.

  4. Forecast of technological development in ore mining branches on basis of modernization of technique and technology of mining practice

    Directory of Open Access Journals (Sweden)

    Alexandr Ivanovich Tatarkin

    2012-12-01

    Full Text Available Pro forma data for exploration of mineral raw material base of some territories Ural regions are given. Perspective trajectories of development of mineral raw complex of federal subjects Russia’s are characterized. On this basis are highlighted groups of region — saving, reducing, raising the level of raw specialization or diversifying own economy. Peculiarities of forecasting technological development in ore mining branches in the period of planned economy of the USSR are illustrated. Indexes of successfully working iron-ore open casts are given. Theraise of internal Russian demand on raw is grounded, for which satisfaction are needed:corresponding tax policy for innovative production, corporate innovative policy, creating fund of supporting ore mining, commercialization of scientific products and working out rules in the sphere of scientific and technical progress. Peculiarities of technological development of ore mining branches of industry are emphasized: necessity of modernization of enterprises and exploration of deposits in regions with not enough developed infrastructure and opportunity of using new system approach by forecasting. Thestages ofeffectiveexploration of deposits and the parameters of developing ore mining enterprises arefound out. Components of forecast of technological development are analyzed. World tendencies of developing ore mining industry and basic directions of forecast of developing ore mining branches are given. New effective technologies being used in the ore mining are analyzed in detail.

  5. DISEASES: text mining and data integration of disease-gene associations.

    Science.gov (United States)

    Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

    2015-03-01

    Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Annotated chemical patent corpus: a gold standard for text mining.

    Directory of Open Access Journals (Sweden)

    Saber A Akhondi

    Full Text Available Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.

  7. Text mining applications in psychiatry: a systematic literature review.

    Science.gov (United States)

    Abbe, Adeline; Grouin, Cyril; Zweigenbaum, Pierre; Falissard, Bruno

    2016-06-01

    The expansion of biomedical literature is creating the need for efficient tools to keep pace with increasing volumes of information. Text mining (TM) approaches are becoming essential to facilitate the automated extraction of useful biomedical information from unstructured text. We reviewed the applications of TM in psychiatry, and explored its advantages and limitations. A systematic review of the literature was carried out using the CINAHL, Medline, EMBASE, PsycINFO and Cochrane databases. In this review, 1103 papers were screened, and 38 were included as applications of TM in psychiatric research. Using TM and content analysis, we identified four major areas of application: (1) Psychopathology (i.e. observational studies focusing on mental illnesses) (2) the Patient perspective (i.e. patients' thoughts and opinions), (3) Medical records (i.e. safety issues, quality of care and description of treatments), and (4) Medical literature (i.e. identification of new scientific information in the literature). The information sources were qualitative studies, Internet postings, medical records and biomedical literature. Our work demonstrates that TM can contribute to complex research tasks in psychiatry. We discuss the benefits, limits, and further applications of this tool in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  8. Construction accident narrative classification: An evaluation of text mining techniques.

    Science.gov (United States)

    Goh, Yang Miang; Ubeynarayana, C U

    2017-11-01

    Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classify accident and near miss narratives will be very significant. This study aims to evaluate the utility of various text mining classification techniques in classifying 1000 publicly available construction accident narratives obtained from the US OSHA website. The study evaluated six machine learning algorithms, including support vector machine (SVM), linear regression (LR), random forest (RF), k-nearest neighbor (KNN), decision tree (DT) and Naive Bayes (NB), and found that SVM produced the best performance in classifying the test set of 251 cases. Further experimentation with tokenization of the processed text and non-linear SVM were also conducted. In addition, a grid search was conducted on the hyperparameters of the SVM models. It was found that the best performing classifiers were linear SVM with unigram tokenization and radial basis function (RBF) SVM with uni-gram tokenization. In view of its relative simplicity, the linear SVM is recommended. Across the 11 labels of accident causes or types, the precision of the linear SVM ranged from 0.5 to 1, recall ranged from 0.36 to 0.9 and F1 score was between 0.45 and 0.92. The reasons for misclassification were discussed and suggestions on ways to improve the performance were provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Geophysical Technologies to Image Old Mine Works

    Energy Technology Data Exchange (ETDEWEB)

    Kanaan Hanna; Jim Pfeiffer [ZapataEngineering, Blackhawk Division, Golden, CO (USA)

    2007-01-15

    ZapataEngineering, Blackhawk Division performed geophysical void detection demonstrations for the US Department of Labor Mine Safety and Health Administration (MSHA). The objective was to advance current state-of-practices of geophysical technologies for detecting underground mine voids. The presence of old mine works above, adjacent, or below an active mine presents major health and safety hazards to miners who have inadvertently cut into locations with such features. In addition, the presence of abandoned mines or voids beneath roadways and highway structures may greatly impact the performance of the transportation infrastructure in terms of cost and public safety. Roads constructed over abandoned mines are subject to potential differential settlement, subsidence, sinkholes, and/or catastrophic collapse. Thus, there is a need to utilize geophysical imaging technologies to accurately locate old mine works. Several surface and borehole geophysical imaging methods and mapping techniques were employed at a known abandoned coal mine in eastern Illinois to investigate which method best map the location and extent of old works. These methods included: 1) high-resolution seismic (HRS) using compressional P-wave (HRPW) and S-wave (HRSW) reflection collected with 3-D techniques; 2) crosshole seismic tomography (XHT); 3) guided waves; 4) reverse vertical seismic profiling (RVSP); and 5) borehole sonar mapping. In addition, several exploration borings were drilled to confirm the presence of the imaged mine voids. The results indicated that the RVSP is the most viable method to accurately detect the subsurface voids with horizontal accuracy of two to five feet. This method was then applied at several other locations in Colorado with various topographic, geologic, and cultural settings for the same purpose. This paper presents the significant results obtained from the geophysical investigations in Illinois.

  10. Event-based text mining for biology and functional genomics

    Science.gov (United States)

    Thompson, Paul; Nawaz, Raheel; McNaught, John; Kell, Douglas B.

    2015-01-01

    The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research. PMID:24907365

  11. Supporting the education evidence portal via text mining

    Science.gov (United States)

    Ananiadou, Sophia; Thompson, Paul; Thomas, James; Mu, Tingting; Oliver, Sandy; Rickinson, Mark; Sasaki, Yutaka; Weissenbacher, Davy; McNaught, John

    2010-01-01

    The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. PMID:20643679

  12. New challenges for text mining: mapping between text and manually curated pathways

    Science.gov (United States)

    Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

    2008-01-01

    Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

  13. Transfer of terrestrial technology for lunar mining

    Science.gov (United States)

    Hall, Robert A.; Green, Patricia A.

    The functions, operational procedures, and major items of equipment that comprise the terrestrial mining process are characterized. These data are used to synthesize a similar activity on the lunar surface. Functions, operations, and types of equipment that can be suitably transferred to lunar operation are identified. Shortfalls, enhancements, and technology development needs are described. The lunar mining process and what is required to adapt terrestrial equipment are highlighted. It is concluded that translation of terrestrial mining equipment and operational processes to perform similar functions on the lunar surface is practical. Adequate attention must be given to the harsh environment and logistical constraints of the lunar setting. By using earth-based equipment as a forcing function, near- and long-term benefits are derived (i.e., improved terrestrial mining in the near term vis-a-vis commercial production of helium-3 in the long term.

  14. Seqenv: linking sequences to environments through text mining

    Directory of Open Access Journals (Sweden)

    Lucas Sinclair

    2016-12-01

    Full Text Available Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

  15. Mine Warfare History and Technology

    Science.gov (United States)

    1975-07-01

    off, it released a spring-driven plunger which struck a " fulminating charge," thus exploding the mine. The discovery of fulminate of mercury (Hg(0NC...2> had been reported to the Royal Society in 1800 by Edward Charles Howard, FRS (brother of the 12th Duke of Norfolk). The use of fulminate of mer...generally a mixture of the two ( fulminate and potassium chlorate) in a small tube impacted at the closed end, the open end being in contact with

  16. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

    Science.gov (United States)

    Westergaard, David; Stærfeldt, Hans-Henrik; Tønsberg, Christian; Jensen, Lars Juhl; Brunak, Søren

    2018-02-01

    Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

  17. Human-centered text mining: a new software system

    NARCIS (Netherlands)

    Poelmans, J.; Elzinga, P.; Neznanov, A.A.; Dedene, G.; Viaene, S.; Kuznetsov, S.

    2012-01-01

    In this paper we introduce a novel human-centered data mining software system which was designed to gain intelligence from unstructured textual data. The architecture takes its roots in several case studies which were a collaboration between the Amsterdam-Amstelland Police, GasthuisZusters Antwerpen

  18. Using text mining for study identification in systematic reviews: a systematic review of current approaches.

    Science.gov (United States)

    O'Mara-Eves, Alison; Thomas, James; McNaught, John; Miwa, Makoto; Ananiadou, Sophia

    2015-01-14

    The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities. Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged? We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings. The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable. On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall). Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously

  19. Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.

    Science.gov (United States)

    Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

    2008-04-25

    The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. The TFIDF term recognition is available as Web Service, described at http://gopubmed4.biotec.tu-dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl.

  20. Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan.

    Science.gov (United States)

    Olofsson, Hanna; Brolund, Agneta; Hellberg, Christel; Silverstein, Rebecca; Stenström, Karin; Österberg, Marie; Dagerhamn, Jessica

    2017-09-01

    One time-consuming aspect of conducting systematic reviews is the task of sifting through abstracts to identify relevant studies. One promising approach for reducing this burden uses text mining technology to identify those abstracts that are potentially most relevant for a project, allowing those abstracts to be screened first. To examine the effectiveness of the text mining functionality of the abstract screening tool Rayyan. User experiences were collected. Rayyan was used to screen abstracts for 6 reviews in 2015. After screening 25%, 50%, and 75% of the abstracts, the screeners logged the relevant references identified. A survey was sent to users. After screening half of the search result with Rayyan, 86% to 99% of the references deemed relevant to the study were identified. Of those studies included in the final reports, 96% to 100% were already identified in the first half of the screening process. Users rated Rayyan 4.5 out of 5. The text mining function in Rayyan successfully helped reviewers identify relevant studies early in the screening process. Copyright © 2017 John Wiley & Sons, Ltd.

  1. Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

    Science.gov (United States)

    Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H.

    2014-01-01

    Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance. PMID:25151493

  2. Development of a Text Mining System for the Discussion of Proactive Aging Management in Nuclear Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Shiraishi, Natsuki; Takahashi, Makoto; Wakabayashi, Toshio [Tohoku University, Tohoku (Japan)

    2011-08-15

    The purpose of this study is to develop an effective system to support the exploration process of knowledge extraction from the database of incident records in the long-operated nuclear power plants with text mining technology, especially for the Generic Issues for proactive materials degradation management (PMDM) project in Japan. A modified system with text mining technology has been developed to support to explore relationships of keywords as cues for the discussion of Generic Issues effectively. As a result of evaluation, the knowledge extraction method with the modified system has been confirmed to support to explore relationships of keywords more effectively than the proposed method in the previous study.

  3. Analysis of patents on mining technology

    Energy Technology Data Exchange (ETDEWEB)

    Menyailo, N.I.; Grishchenko, A.N.; Ratner, M.V.; Kobylyanskii, A.Ya.; Tyshlek, E.G.

    1987-09-01

    Analyses the current work being carried out with the aim of developing and perfecting coal mining technology with regard to improving safety and working conditions (equipment is currently responsible for 6.3% of all hazards in coal mines) by examining patents of class ES 21 S produced in the USSR, USA, UK, FRG, Japan and France between 1970-1984. By far the majority of patents is concerned with improving technology and productivity and a disappointing number deals with safety matters (only 7.2% of the patents for new cutter loader designs deal with dust suppression systems and most of these come from the FRG; no patents for powered mining complexes deal with the problem of noise and vibration reduction). The patents with the most direct relevance to health and safety concern remote control devices for mining equipment, in particular, devices based on radioactive isotopes (e.g. cesium-137, americum-241, selenium-75, californium-252) but measures for monitoring them and protecting against them are not found.

  4. Standardized UXO Technology Demonstration Site Mine Grid Scoring Record #837

    National Research Council Canada - National Science Library

    Teefy, Dennis

    2007-01-01

    ...) utilizing the APG Standardized UXO Technology Demonstration Site Mine Grid. This Scoring Record was coordinated by Dennis Teefy and the Standardized UXO Technology Demonstration Site Scoring Committee...

  5. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

    DEFF Research Database (Denmark)

    Westergaard, David; Stærfeldt, Hans Henrik; Tønsberg, Christian

    2018-01-01

    million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein......-text articles consistently outperforms using abstracts only....

  6. pubmed.mineR: An R package with text-mining algorithms to ...

    Indian Academy of Sciences (India)

    mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we ...

  7. Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

    Science.gov (United States)

    Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang

    2015-06-06

    Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating

  8. Roles for text mining in protein function prediction.

    Science.gov (United States)

    Verspoor, Karin M

    2014-01-01

    The Human Genome Project has provided science with a hugely valuable resource: the blueprints for life; the specification of all of the genes that make up a human. While the genes have all been identified and deciphered, it is proteins that are the workhorses of the human body: they are essential to virtually all cell functions and are the primary mechanism through which biological function is carried out. Hence in order to fully understand what happens at a molecular level in biological organisms, and eventually to enable development of treatments for diseases where some aspect of a biological system goes awry, we must understand the functions of proteins. However, experimental characterization of protein function cannot scale to the vast amount of DNA sequence data now available. Computational protein function prediction has therefore emerged as a problem at the forefront of modern biology (Radivojac et al., Nat Methods 10(13):221-227, 2013).Within the varied approaches to computational protein function prediction that have been explored, there are several that make use of biomedical literature mining. These methods take advantage of information in the published literature to associate specific proteins with specific protein functions. In this chapter, we introduce two main strategies for doing this: association of function terms, represented as Gene Ontology terms (Ashburner et al., Nat Genet 25(1):25-29, 2000), to proteins based on information in published articles, and a paradigm called LEAP-FS (Literature-Enhanced Automated Prediction of Functional Sites) in which literature mining is used to validate the predictions of an orthogonal computational protein function prediction method.

  9. Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

    Science.gov (United States)

    Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

    2016-04-26

    Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

  10. Mining free-text medical records for companion animal enteric syndrome surveillance.

    Science.gov (United States)

    Anholt, R M; Berezowski, J; Jamal, I; Ribble, C; Stephen, C

    2014-03-01

    Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Analyzing asset management data using data and text mining.

    Science.gov (United States)

    2014-07-01

    Predictive models using text from a sample competitively bid California highway projects have been used to predict a construction : projects likely level of cost overrun. A text description of the project and the text of the five largest project line...

  12. Opinion Mining in Latvian Text Using Semantic Polarity Analysis and Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Gatis Špats

    2016-07-01

    Full Text Available In this paper we demonstrate approaches for opinion mining in Latvian text. Authors have applied, combined and extended results of several previous studies and public resources to perform opinion mining in Latvian text using two approaches, namely, semantic polarity analysis and machine learning. One of the most significant constraints that make application of opinion mining for written content classification in Latvian text challenging is the limited publicly available text corpora for classifier training. We have joined several sources and created a publically available extended lexicon. Our results are comparable to or outperform current achievements in opinion mining in Latvian. Experiments show that lexicon-based methods provide more accurate opinion mining than the application of Naive Bayes machine learning classifier on Latvian tweets. Methods used during this study could be further extended using human annotators, unsupervised machine learning and bootstrapping to create larger corpora of classified text.

  13. Text mining for adverse drug events: the promise, challenges, and state of the art.

    Science.gov (United States)

    Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H

    2014-10-01

    Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

  14. Negation scope and spelling variation for text-mining of Danish electronic patient records

    DEFF Research Database (Denmark)

    Thomas, Cecilia Engel; Jensen, Peter Bjødstrup; Werge, Thomas

    2014-01-01

    Electronic patient records are a potentially rich data source for knowledge extraction in biomedical research. Here we present a method based on the ICD10 system for text-mining of Danish health records. We have evaluated how adding functionalities to a baseline text-mining tool affected...

  15. Mining knowledge from text repositories using information extraction ...

    Indian Academy of Sciences (India)

    Department of Computer Science, Shri Shivaji Science and Arts College, Chikhli 443 201, India; Department of Computer Science, S K Porwal College, Kamptee, Nagpur 441 002, India; P G Department of Computer Science and Technology, Degree College of Physical Education, Hanuman Vyayam Prasarak Mandal, ...

  16. Searching for Significance in Unstructured Data: Text Mining with Leximancer

    Science.gov (United States)

    Thomas, David A.

    2014-01-01

    Scholars in many knowledge domains rely on sophisticated information technologies to search for and retrieve records and publications pertinent to their research interests. But what is a scholar to do when a search identifies hundreds of documents, any of which might be vital or irrelevant to his or her work? The problem is further complicated by…

  17. Machine learning approach for text and document mining

    OpenAIRE

    Bijalwan, Vishwanath; Kumari, Pinki; Pascual, Jordan; Semwal, Vijay Bhaskar

    2014-01-01

    Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and ...

  18. Text mining for search term development in systematic reviewing: A discussion of some methods and challenges.

    Science.gov (United States)

    Stansfield, Claire; O'Mara-Eves, Alison; Thomas, James

    2017-09-01

    Using text mining to aid the development of database search strings for topics described by diverse terminology has potential benefits for systematic reviews; however, methods and tools for accomplishing this are poorly covered in the research methods literature. We briefly review the literature on applications of text mining for search term development for systematic reviewing. We found that the tools can be used in 5 overarching ways: improving the precision of searches; identifying search terms to improve search sensitivity; aiding the translation of search strategies across databases; searching and screening within an integrated system; and developing objectively derived search strategies. Using a case study and selected examples, we then reflect on the utility of certain technologies (term frequency-inverse document frequency and Termine, term frequency, and clustering) in improving the precision and sensitivity of searches. Challenges in using these tools are discussed. The utility of these tools is influenced by the different capabilities of the tools, the way the tools are used, and the text that is analysed. Increased awareness of how the tools perform facilitates the further development of methods for their use in systematic reviews. Copyright © 2017 John Wiley & Sons, Ltd.

  19. A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives

    Directory of Open Access Journals (Sweden)

    Said A. Salloum

    2017-01-01

    Full Text Available Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR and data mining. Natural Language Processing (NLP techniques were used to extract knowledge from the textual text that is written by human beings. Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period. Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other. It becomes a common practice to not write a sentence with correct grammar and spelling. This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order. Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites. This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data. This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world. Results of this survey can serve as the baselines for future text mining research.

  20. System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

    Directory of Open Access Journals (Sweden)

    Mingzhi Liao

    2014-01-01

    Full Text Available Liuwei-dihuang (LWDH is widely used in traditional Chinese medicine (TCM, but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM.

  1. Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis

    Directory of Open Access Journals (Sweden)

    Alexandra Amado

    2018-01-01

    Full Text Available Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents, Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.

  2. The application of modern surveying technology in mining survey

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Jin-feng; Song, Wei-dong; Zhang, Ji-chao; Zhang, Dong-mei [Liaoning Technical University, Fuxin (China). School of Geomatics

    2008-06-15

    With the unceasing appearance and widespread application of new surveying technology, the present age mining survey has meet huge change. However, lots of problems occurred while using the new techniques since the number of mine is large in China and condition of the mine district is complex, it in some sense influenced the mine exploitation and management of China. The paper summarized the present situation of new technical application in mining survey, including the advanced instrumentation equipment, the '3S' technology, the information and the network technology and the information fusion technology and so on, and analyzed the problems which exists in the current mining survey, it also provided new ways to present age mining survey from the sustainable development angle. 5 refs.

  3. Throw the bath water out, keep the baby: keeping medically-relevant terms for text mining.

    Science.gov (United States)

    Jarman, Jay; Berndt, Donald J

    2010-11-13

    The purpose of this research is to answer the question, can medically-relevant terms be extracted from text notes and text mined for the purpose of classification and obtain equal or better results than text mining the original note? A novel method is used to extract medically-relevant terms for the purpose of text mining. A dataset of 5,009 EMR text notes (1,151 related to falls) was obtained from a Veterans Administration Medical Center. The dataset was processed with a natural language processing (NLP) application which extracted concepts based on SNOMED-CT terms from the Unified Medical Language System (UMLS) Metathesaurus. SAS Enterprise Miner was used to text mine both the set of complete text notes and the set represented by the extracted concepts. Logistic regression models were built from the results, with the extracted concept model performing slightly better than the complete note model.

  4. Improving classification in protein structure databases using text mining

    Directory of Open Access Journals (Sweden)

    Jones David T

    2009-05-01

    Full Text Available Abstract Background The classification of protein domains in the CATH resource is primarily based on structural comparisons, sequence similarity and manual analysis. One of the main bottlenecks in the processing of new entries is the evaluation of 'borderline' cases by human curators with reference to the literature, and better tools for helping both expert and non-expert users quickly identify relevant functional information from text are urgently needed. A text based method for protein classification is presented, which complements the existing sequence and structure-based approaches, especially in cases exhibiting low similarity to existing members and requiring manual intervention. The method is based on the assumption that textual similarity between sets of documents relating to proteins reflects biological function similarities and can be exploited to make classification decisions. Results An optimal strategy for the text comparisons was identified by using an established gold standard enzyme dataset. Filtering of the abstracts using a machine learning approach to discriminate sentences containing functional, structural and classification information that are relevant to the protein classification task improved performance. Testing this classification scheme on a dataset of 'borderline' protein domains that lack significant sequence or structure similarity to classified proteins showed that although, as expected, the structural similarity classifiers perform better on average, there is a significant benefit in incorporating text similarity in logistic regression models, indicating significant orthogonality in this additional information. Coverage was significantly increased especially at low error rates, which is important for routine classification tasks: 15.3% for the combined structure and text classifier compared to 10% for the structural classifier alone, at 10-3 error rate. Finally when only the highest scoring predictions were used

  5. An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

    Science.gov (United States)

    Trybula, Walter J.; Wyllys, Ronald E.

    2000-01-01

    Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

  6. Seqenv: linking sequences to environments through text mining

    Czech Academy of Sciences Publication Activity Database

    Sinclair, L.; Ijaz, U.Z.; Jensen, L.J.; Coolen, M.J.L.; Gubry-Rangin, C.; Chroňáková, Alica; Oulas, A.; Pavloudi, Ch.; Schnetzer, J.; Weimann, A.; Ijaz, A.; Eiler, A.; Quince, Ch.; Pafilis, E.

    2016-01-01

    Roč. 4, December (2016), č. článku e2690. ISSN 2167-8359 Institutional support: RVO:60077344 Keywords : bioinformatics * ecology * microbiology * genomics * sequence analysis * text processing Subject RIV: EH - Ecology, Behaviour Impact factor: 2.177, year: 2016

  7. Signal Detection Framework Using Semantic Text Mining Techniques

    Science.gov (United States)

    Sudarsan, Sithu D.

    2009-01-01

    Signal detection is a challenging task for regulatory and intelligence agencies. Subject matter experts in those agencies analyze documents, generally containing narrative text in a time bound manner for signals by identification, evaluation and confirmation, leading to follow-up action e.g., recalling a defective product or public advisory for…

  8. PubRunner: A light-weight framework for updating text mining results.

    Science.gov (United States)

    Anekalla, Kishore R; Courneya, J P; Fiorini, Nicolas; Lever, Jake; Muchow, Michael; Busby, Ben

    2017-01-01

    Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications.

  9. Web services-based text-mining demonstrates broad impacts for interoperability and process simplification

    Science.gov (United States)

    Wiegers, Thomas C.; Davis, Allan Peter; Mattingly, Carolyn J.

    2014-01-01

    disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/ PMID:24919658

  10. Web services-based text-mining demonstrates broad impacts for interoperability and process simplification.

    Science.gov (United States)

    Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

    2014-01-01

    disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/ © The Author(s) 2014. Published by Oxford University Press.

  11. A Survey of Topic Modeling in Text Mining

    OpenAIRE

    Rubayyi Alghamdi; Khalid Alfalqi

    2015-01-01

    Topic models provide a convenient way to analyze large of unclassified text. A topic contains a cluster of words that frequently occur together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be under the field of topic modeling. First one discusses the area of methods of topic modeling, which has four methods that can be considerable under this category. These methods are Laten...

  12. Acquisition Program Problem Detection Using Text Mining Methods

    Science.gov (United States)

    2012-03-01

    this method into their practices (Berry & Kogan, 2010). Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent...also known as Latent Semantic Indexing, uses a series of three matrices (document eigenvector, eigenvalue, and term eigenvector) to approximate the...Estimate at Complete • EVM: Earned Value Management • HTML: Hyper Text Markup Language • LDA: Latent Dirichlet Allocation • LSA: Latent Semantic Analysis

  13. PaperBLAST: Text Mining Papers for Information about Homologs.

    Science.gov (United States)

    Price, Morgan N; Arkin, Adam P

    2017-01-01

    Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST's database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins' functions.

  14. Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

    DEFF Research Database (Denmark)

    Debortoli, Stefan; Müller, Oliver; Junglas, Iris

    2016-01-01

    , such as manual coding. Yet, the size of text data setsobtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challengesencountered when applying automated text-mining techniques in information systems research. In particular, weshowcase the use of probabilistic...... researchers,this tutorial provides some guidance for conducting text mining studies on their own and for evaluating the quality ofothers....

  15. Data Mining of Causal Relations from Text: Analysing Maritime Accident Investigation Reports

    OpenAIRE

    Tirunagari, Santosh

    2015-01-01

    Text mining is a process of extracting information of interest from text. Such a method includes techniques from various areas such as Information Retrieval (IR), Natural Language Processing (NLP), and Information Extraction (IE). In this study, text mining methods are applied to extract causal relations from maritime accident investigation reports collected from the Marine Accident Investigation Branch (MAIB). These causal relations provide information on various mechanisms behind accidents,...

  16. Significance and technology of coal mine packing

    Science.gov (United States)

    Wang, Yunliang

    2017-08-01

    Packing is a kind of mining method which is used to fill the goaf with filling material to control the movement of overlying strata and reduce the deformation of the surface. This paper mainly discusses the significance of coal mine packing, and introduces the current method of filling and mining. Finally, the future of coal mine packing is forecasted.

  17. Text Mining in Python through the HTRC Feature Reader

    Directory of Open Access Journals (Sweden)

    Peter Organisciak

    2016-11-01

    Full Text Available We introduce a toolkit for working with the 13.6 million volume Extracted Features Dataset from the HathiTrust Research Center. You will learn how to peer at the words and trends of any book in the collection, while developing broadly useful Python data analysis skills. The HathiTrust holds nearly 15 million digitized volumes from libraries around the world. In addition to their individual value, these works in aggregate are extremely valuable for historians. Spanning many centuries and genres, they offer a way to learn about large-scale trends in history and culture, as well as evidence for changes in language or even the structure of the book. To simplify access to this collection the HathiTrust Research Center (HTRC has released the Extracted Features dataset (Capitanu et al. 2015: a dataset that provides quantitative information describing every page of every volume in the collection. In this lesson, we introduce the HTRC Feature Reader, a library for working with the HTRC Extracted Features dataset using the Python programming language. The HTRC Feature Reader is structured to support work using popular data science libraries, particularly Pandas. Pandas provides simple structures for holding data and powerful ways to interact with it. The HTRC Feature Reader uses these data structures, so learning how to use it will also cover general data analysis skills in Python.

  18. Text mining to decipher free-response consumer complaints: insights from the NHTSA vehicle owner's complaint database.

    Science.gov (United States)

    Ghazizadeh, Mahtab; McDonald, Anthony D; Lee, John D

    2014-09-01

    This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration's vehicle owner's complaint database. As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.

  19. Classifying unstructed textual data using the Product Score Model: an alternative text mining algorithm

    NARCIS (Netherlands)

    He, Qiwei; Veldkamp, Bernard P.; Eggen, T.J.H.M.; Veldkamp, B.P.

    2012-01-01

    Unstructured textual data such as students’ essays and life narratives can provide helpful information in educational and psychological measurement, but often contain irregularities and ambiguities, which creates difficulties in analysis. Text mining techniques that seek to extract useful

  20. Text and Structural Data Mining of Influenza Mentions in Web and Social Media

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Cook, Diane; Mikler, Armin R.; Singh, Karan P.

    2010-02-22

    Text and structural data mining of Web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5-October-2008 to 21-March-2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like-illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

  1. Text mining and visualization case studies using open-source tools

    CERN Document Server

    Chisholm, Andrew

    2016-01-01

    Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website. The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities.

  2. Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

    Science.gov (United States)

    Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

    2012-05-01

    This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

  3. Text mining tools for extracting information about microbial biodiversity in food

    OpenAIRE

    Deleger, Louise; Bossy, Robert; Nédellec, Claire

    2017-01-01

    Introduction Information on food microbial biodiversity is scattered across millions of scientific papers (2 million references in the PubMed bibliographic database in 2017). It is impossible to manually achieve an exhaustive analysis of these documents. Text-mining and knowledge engineering methods can assist the researcher in finding relevant information. Material & Methods We propose to study bacterial biodiversity using text-mining tools from the Alvis platform. First, w...

  4. MINE WASTE TECHNOLOGY PROGRAM PREVENTION OF ACID MINE DRAINAGE GENERATION FROM OPEN-PIT HIGHWALLS

    Science.gov (United States)

    This document summarizes the results of Mine Waste Technology Program Activity III, Project 26, Prevention of Acid Mine Drainage Generation from Open-Pit Highwalls. The intent of this project was to obtain performance data on the ability of four technologies to prevent the gener...

  5. The Application of Data Mining Technology (Based on literature review)

    OpenAIRE

    Redi, Samson

    2016-01-01

    Data mining technology is the process of taking out hidden information from the big database and facilitate decision making. The objective of this report is to show the applicability of Data mining technology by reviewing sample research works. The first research review is on ATM analysis, it is possible to reach the following conclusion, and the predictive data mining is helpful for analysis of ATM data in order to improve the quality of service that Banks provide to their customer. The ...

  6. TEXT MINING: TEXT SIMILARITY MEASURE FOR NEWS ARTICLES BASED ON STRING BASED APPROACH

    OpenAIRE

    R. Kohila*, Dr. K. Arunesh

    2016-01-01

    Now-a-days, the documents similarity measuring plays an important role in text related researches. There are many applications in document similarity measures such as plagiarism detection, document clustering, automatic essay scoring, information retrieval and machine translation. String Based Similarity, Knowledge Based Similarity and Corpus Based Similarity are the three major approaches proposed by the most of the   researchers to solve the problems in document similarity. In thi...

  7. Automated detection of follow-up appointments using text mining of discharge records.

    Science.gov (United States)

    Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

    2010-06-01

    To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

  8. Data Mining Based on Cloud-Computing Technology

    Directory of Open Access Journals (Sweden)

    Ren Ying

    2016-01-01

    Full Text Available There are performance bottlenecks and scalability problems when traditional data-mining system is used in cloud computing. In this paper, we present a data-mining platform based on cloud computing. Compared with a traditional data mining system, this platform is highly scalable, has massive data processing capacities, is service-oriented, and has low hardware cost. This platform can support the design and applications of a wide range of distributed data-mining systems.

  9. Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies

    Science.gov (United States)

    2013-01-01

    Background The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? Results We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. aFor text mining, preprocessing the EHR corpus with

  10. Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

    Science.gov (United States)

    Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

    2013-01-16

    The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields

  11. Alkemio: association of chemicals with biomedical topics by text and data mining

    OpenAIRE

    Gijon-Correas, J.A.; Andrade-Navarro, M. A.; Fontaine, J F

    2014-01-01

    The PubMed(R) database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness...

  12. Managing biological networks by using text mining and computer-aided curation

    Science.gov (United States)

    Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

    2015-11-01

    In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

  13. Integration and publication of heterogeneous text-mined relationships on the Semantic Web.

    Science.gov (United States)

    Coulet, Adrien; Garten, Yael; Dumontier, Michel; Altman, Russ B; Musen, Mark A; Shah, Nigam H

    2011-05-17

    Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering. We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network. The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE.

  14. A framework of Chinese semantic text mining based on ontology learning

    Science.gov (United States)

    Zhang, Yu-feng; Hu, Feng

    2012-01-01

    Text mining and ontology learning can be effectively employed to acquire the Chinese semantic information. This paper explores a framework of semantic text mining based on ontology learning to find the potential semantic knowledge from the immensity text information on the Internet. This framework consists of four parts: Data Acquisition, Feature Extraction, Ontology Construction, and Text Knowledge Pattern Discovery. Then the framework is applied into an actual case to try to find out the valuable information, and even to assist the consumers with selecting proper products. The results show that this framework is reasonable and effective.

  15. Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

    DEFF Research Database (Denmark)

    Debortoli, Stefan; Müller, Oliver; Junglas, Iris

    2016-01-01

    , such as manual coding. Yet, the size of text data setsobtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challengesencountered when applying automated text-mining techniques in information systems research. In particular, weshowcase the use of probabilistic...... topic modeling via Latent Dirichlet Allocation, an unsupervised text miningtechnique, in combination with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifactby automatically analyzing more than 12,000 online customer reviews. For fellow information systems...... researchers,this tutorial provides some guidance for conducting text mining studies on their own and for evaluating the quality ofothers....

  16. Compatibility between Text Mining and Qualitative Research in the Perspectives of Grounded Theory, Content Analysis, and Reliability

    Science.gov (United States)

    Yu, Chong Ho; Jannasch-Pennell, Angel; DiGangi, Samuel

    2011-01-01

    The objective of this article is to illustrate that text mining and qualitative research are epistemologically compatible. First, like many qualitative research approaches, such as grounded theory, text mining encourages open-mindedness and discourages preconceptions. Contrary to the popular belief that text mining is a linear and fully automated…

  17. WIRELESS MINE-WIDE TELECOMMUNICATIONS TECHNOLOGY

    Energy Technology Data Exchange (ETDEWEB)

    Zvi H. Meiksin

    2004-03-01

    A comprehensive mine-wide, two-way wireless voice and data communication system for the underground mining industry was developed. The system achieves energy savings through increased productivity and greater energy efficiency in meeting safety requirements within mines. The mine-wide system is comprised of two interfaced subsystems: a through-the-earth communications system and an in-mine communications system. The mine-wide system permits two-way communication among underground personnel and between underground and surface personnel. The system was designed, built, and commercialized. Several systems are in operation in underground mines in the United States. The use of these systems has proven they result in considerable energy savings. A system for tracking the location of vehicles and people within the mine was also developed, built and tested successfully. Transtek's systems are being used by the National Institute of Occupational Safety and Health (NIOSH) in their underground mine rescue team training program. This project also resulted in a spin-off rescue team lifeline and communications system. Furthermore, the project points the way to further developments that can lead to a GPS-like system for underground mines allowing the use of autonomous machines in underground mining operations, greatly reducing the amount of energy used in these operations. Some products developed under this program are transferable to applications in fields other than mining. The rescue team system is applicable to use by first responders to natural, accidental, or terrorist-caused building collapses. The in-mine communications system can be installed in high-rise buildings providing in-building communications to security and maintenance personnel as well as to first responders.

  18. Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II.

    Science.gov (United States)

    Lu, Zhiyong; Hirschman, Lynette

    2012-01-01

    Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators. DATABASE URL: http://www.biocreative.org/tasks/bc-workshop-2012/workflow/.

  19. Some implications of in situ uranium mining technology development

    Energy Technology Data Exchange (ETDEWEB)

    Cowan, C.E.; Parkhurst, M.A.; Cole, R.J.; Keller, D.; Mellinger, P.J.; Wallace, R.W.

    1980-09-01

    A technology assessment was initiated in March 1979 of the in-situ uranium mining technology. This report explores the impediments to development and deployment of this technology and evaluates the environmental impacts of a generic in-situ facility. The report is divided into the following sections: introduction, technology description, physical environment, institutional and socioeconomic environment, impact assessment, impediments, and conclusions. (DLC)

  20. Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining.

    Science.gov (United States)

    Yu, Shi; Van Vooren, Steven; Tranchevent, Leon-Charles; De Moor, Bart; Moreau, Yves

    2008-08-15

    Computational gene prioritization methods are useful to help identify susceptibility genes potentially being involved in genetic disease. Recently, text mining techniques have been applied to extract prior knowledge from text-based genomic information sources and this knowledge can be used to improve the prioritization process. However, the effect of various vocabularies, representations and ranking algorithms on text mining for gene prioritization is still an issue that requires systematic and comparative studies. Therefore, a benchmark study about the vocabularies, representations and ranking algorithms in gene prioritization by text mining is discussed in this article. We investigated 5 different domain vocabularies, 2 text representation schemes and 27 linear ranking algorithms for disease gene prioritization by text mining. We indexed 288 177 MEDLINE titles and abstracts with the TXTGate text pro.ling system and adapted the benchmark dataset of the Endeavour gene prioritization system that consists of 618 disease-causing genes. Textual gene pro.les were created and their performance for prioritization were evaluated and discussed in a comparative manner. The results show that inverse document frequency-based representation of gene term vectors performs better than the term-frequency inverse document-frequency representation. The eVOC and MESH domain vocabularies perform better than Gene Ontology, Online Mendelian Inheritance in Man's and London Dysmorphology Database. The ranking algorithms based on 1-SVM, Standard Correlation and Ward linkage method provide the best performance. The MATLAB code of the algorithm and benchmark datasets are available by request. Supplementary data are available at Bioinformatics online.

  1. Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.

    Science.gov (United States)

    Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C; Greene, Anna C; Greene, Casey S

    2016-01-01

    Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. © The Author 2015. Published by Oxford University Press.

  2. DrugQuest - a text mining workflow for drug association discovery.

    Science.gov (United States)

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

    2016-06-06

    Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

  3. Implementation of state-of-art mining knowledge and technologies in design and operation of a safe and efficient deep gold mine stope for 21st Century.

    CSIR Research Space (South Africa)

    Van der Merwe, JN

    2001-02-01

    Full Text Available Final Project Report Implementation of state-of-art mining knowledge and technologies in design and operation of a safe and efficient deep gold mine stope for 21st Century van der Merwe, J. N., Wojno, L. and Toper, A. Z. Research agency : Rock... than 15 m face advance per month, dilution and gold losses reduced by 15 % compared to adjacent stoping, consistent provision of recommended environmental conditions and finally cost effectiveness. The main areas of concern with current mining...

  4. Membrane technology applied to acid mine drainage from copper mining.

    Science.gov (United States)

    Ambiado, K; Bustos, C; Schwarz, A; Bórquez, R

    2017-02-01

    The objective of this study is to evaluate the treatment of high-strength acid mine drainage (AMD) from copper mining by nanofiltration (NF) and reverse osmosis (RO) at pilot scale. The performances of two commercial spiral-wound membranes - NF99 and RO98pHt, both from Alfa Laval - were compared. The effects of pressure and feed flow on ion rejection and permeate flux were evaluated. The results showed high ion removal under optimum pressure conditions, which reached 92% for the NF99 membrane and 98% for the RO98pHt membrane. Sulfate removal reached 97% and 99% for NF99 and RO98pHt, respectively. In the case of copper, aluminum, iron and manganese, the removal percentage surpassed 95% in both membranes. Although concentration polarization limited NF performance at higher pressures, permeate fluxes observed in NF were five times greater than those obtained by RO, with only slightly lower divalent ion rejection rates, making it a promising option for the treatment of AMD.

  5. BioCreative Workshops for DOE Genome Sciences: Text Mining for Metagenomics

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Cathy H. [Univ. of Delaware, Newark, DE (United States). Center for Bioinformatics and Computational Biology; Hirschman, Lynette [The MITRE Corporation, Bedford, MA (United States)

    2016-10-29

    The objective of this project was to host BioCreative workshops to define and develop text mining tasks to meet the needs of the Genome Sciences community, focusing on metadata information extraction in metagenomics. Following the successful introduction of metagenomics at the BioCreative IV workshop, members of the metagenomics community and BioCreative communities continued discussion to identify candidate topics for a BioCreative metagenomics track for BioCreative V. Of particular interest was the capture of environmental and isolation source information from text. The outcome was to form a “community of interest” around work on the interactive EXTRACT system, which supported interactive tagging of environmental and species data. This experiment is included in the BioCreative V virtual issue of Database. In addition, there was broad participation by members of the metagenomics community in the panels held at BioCreative V, leading to valuable exchanges between the text mining developers and members of the metagenomics research community. These exchanges are reflected in a number of the overview and perspective pieces also being captured in the BioCreative V virtual issue. Overall, this conversation has exposed the metagenomics researchers to the possibilities of text mining, and educated the text mining developers to the specific needs of the metagenomics community.

  6. Vaccine adverse event text mining system for extracting features from vaccine safety reports.

    Science.gov (United States)

    Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

    2012-01-01

    To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

  7. Creating Knowledgebases to Text-Mine PUBMED Articles Using Clustering Techniques

    Science.gov (United States)

    Crasto, Chiquito J; Morse, Thomas M.; Migliore, Michele; Nadkarni, Prakash; Hines, Michael; Brash, Douglas E.; Miller, Perry L.; Shepherd, Gordon M.

    2003-01-01

    Knowledgebase-mediated text -mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing. PMID:14728326

  8. Deploying mutation impact text-mining software with the SADI Semantic Web Services framework.

    Science.gov (United States)

    Riazanov, Alexandre; Laurila, Jonas Bergman; Baker, Christopher J O

    2011-01-01

    Mutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining. This article explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data. SADI is a set of conventions for creating web services with semantic descriptions that facilitate automatic discovery and orchestration. We describe a case study exploring and demonstrating the utility of the SADI approach in our context. We describe several SADI services we created based on our text mining API and data, and demonstrate how they can be used in a number of biologically meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we pay special attention to the integration of mutation impact services with external SADI services providing information about related biological entities, such as proteins, pathways, and drugs. We have identified that SADI provides an effective way of exposing our mutation impact data such that it can be leveraged by a variety of stakeholders in multiple use cases. The solutions we provide for our use cases can serve as examples to potential SADI adopters trying to solve similar integration problems.

  9. Mining for associations between text and brain activation in a functional neuroimaging database

    DEFF Research Database (Denmark)

    Nielsen, Finn Årup; Hansen, Lars Kai; Balslev, D.

    2004-01-01

    We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach coo...... that the statistically motivated associations are well aligned with general neuroscientific knowledge....

  10. Mining for associations between text and brain activation in a functional neuroimaging database

    DEFF Research Database (Denmark)

    Nielsen, Finn Arup; Hansen, Lars Kai; Balslev, Daniela

    2004-01-01

    We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach...

  11. Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

    Science.gov (United States)

    Jiang, Feng; McComas, William F.

    2014-01-01

    This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were…

  12. The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

    Science.gov (United States)

    Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

    2013-01-01

    The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

  13. Trends of E-Learning Research from 2000 to 2008: Use of Text Mining and Bibliometrics

    Science.gov (United States)

    Hung, Jui-long

    2012-01-01

    This study investigated the longitudinal trends of e-learning research using text mining techniques. Six hundred and eighty-nine (689) refereed journal articles and proceedings were retrieved from the Science Citation Index/Social Science Citation Index database in the period from 2000 to 2008. All e-learning publications were grouped into two…

  14. Complementing the Numbers: A Text Mining Analysis of College Course Withdrawals

    Science.gov (United States)

    Michalski, Greg V.

    2011-01-01

    Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…

  15. Knowledge based word-concept model estimation and refinement for biomedical text mining.

    Science.gov (United States)

    Jimeno Yepes, Antonio; Berlanga, Rafael

    2015-02-01

    Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Text mining approach to predict hospital admissions using early medical records from the emergency department.

    Science.gov (United States)

    Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

    2017-04-01

    Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

  17. Chemical Topic Modeling: Exploring Molecular Data Sets Using a Common Text-Mining Approach.

    Science.gov (United States)

    Schneider, Nadine; Fechner, Nikolas; Landrum, Gregory A; Stiefl, Nikolaus

    2017-08-28

    Big data is one of the key transformative factors which increasingly influences all aspects of modern life. Although this transformation brings vast opportunities it also generates novel challenges, not the least of which is organizing and searching this data deluge. The field of medicinal chemistry is not different: more and more data are being generated, for instance, by technologies such as DNA encoded libraries, peptide libraries, text mining of large literature corpora, and new in silico enumeration methods. Handling those huge sets of molecules effectively is quite challenging and requires compromises that often come at the expense of the interpretability of the results. In order to find an intuitive and meaningful approach to organizing large molecular data sets, we adopted a probabilistic framework called "topic modeling" from the text-mining field. Here we present the first chemistry-related implementation of this method, which allows large molecule sets to be assigned to "chemical topics" and investigating the relationships between those. In this first study, we thoroughly evaluate this novel method in different experiments and discuss both its disadvantages and advantages. We show very promising results in reproducing human-assigned concepts using the approach to identify and retrieve chemical series from sets of molecules. We have also created an intuitive visualization of the chemical topics output by the algorithm. This is a huge benefit compared to other unsupervised machine-learning methods, like clustering, which are commonly used to group sets of molecules. Finally, we applied the new method to the 1.6 million molecules of the ChEMBL22 data set to test its robustness and efficiency. In about 1 h we built a 100-topic model of this large data set in which we could identify interesting topics like "proteins", "DNA", or "steroids". Along with this publication we provide our data sets and an open-source implementation of the new method (CheTo) which

  18. Science and Technology Text Mining: Electric Power Sources

    Science.gov (United States)

    2004-04-01

    and SULFATION. Materials include CARBON, LITHIUM, WATER,, SULFUR, OXYGEN, GRAPHITE , IRON, NITROGEN, NICKEL, AIR, and many others including ALLOYS...ALLOYS, CARBON [ GRAPHITE ], METAL, ACID, NICKEL [NI, NICKEL HYDROXIDE, NICKEL-CADMIUM, HYDROGEN [HYDRIDE]); Processes/ Phenomena (DISCHARGE, CYCLING...into Solid Fossil Fuel Cycle (RAW COAL, ANTHRACITE , QUARTZ REACTOR, COAL COMBUSTION, FLY ASH, EMISSIONS) Gaseous Fossil Fuel Cycle (NATURAL GAS

  19. Science and Technology Text Mining: Mexico Core Competencies

    Science.gov (United States)

    2002-01-01

    A. del Río, and A.M. Ramírez, Analisis De La Evaluacion De Las Revistas Latinoamericanas A Traves Del Factor De Impacto Renormalizado, Rev. Esp. Doc...contam 0.6%, emiss 0.6%, soil 0.5%, mex 0.5%, hidraul 0.5%, epa 0.4%, meteorol 0.4%, concentr 0.4%, ambient 0.4%, ozon 0.4%, toxic 0.4

  20. Data Mining Technologies Inspired from Visual Principle

    Science.gov (United States)

    Xu, Zongben

    In this talk we review the recent work done by our group on data mining (DM) technologies deduced from simulating visual principle. Through viewing a DM problem as a cognition problems and treading a data set as an image with each light point located at a datum position, we developed a series of high efficient algorithms for clustering, classification and regression via mimicking visual principles. In pattern recognition, human eyes seem to possess a singular aptitude to group objects and find important structure in an efficient way. Thus, a DM algorithm simulating visual system may solve some basic problems in DM research. From this point of view, we proposed a new approach for data clustering by modeling the blurring effect of lateral retinal interconnections based on scale space theory. In this approach, as the data image blurs, smaller light blobs merge into large ones until the whole image becomes one light blob at a low enough level of resolution. By identifying each blob with a cluster, the blurring process then generates a family of clustering along the hierarchy. The proposed approach provides unique solutions to many long standing problems, such as the cluster validity and the sensitivity to initialization problems, in clustering. We extended such an approach to classification and regression problems, through combatively employing the Weber's law in physiology and the cell response classification facts. The resultant classification and regression algorithms are proven to be very efficient and solve the problems of model selection and applicability to huge size of data set in DM technologies. We finally applied the similar idea to the difficult parameter setting problem in support vector machine (SVM). Viewing the parameter setting problem as a recognition problem of choosing a visual scale at which the global and local structures of a data set can be preserved, and the difference between the two structures be maximized in the feature space, we derived a

  1. The Text-mining based PubChem Bioassay neighboring analysis.

    Science.gov (United States)

    Han, Lianyi; Suzek, Tugba O; Wang, Yanli; Bryant, Steve H

    2010-11-08

    In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information (i.e. molecular structure, bioactivities) and the unstructured information (such as descriptions of bioassay experiments), has been increasing exponentially. As a result, it has become even more demanding and challenging to efficiently assemble the bioactivity data by mining the huge amount of information to identify and interpret the relationships among the diversified bioassay experiments. In this work, we propose a text-mining based approach for bioassay neighboring analysis from the unstructured text descriptions contained in the PubChem BioAssay database. The neighboring analysis is achieved by evaluating the cosine scores of each bioassay pair and fraction of overlaps among the human-curated neighbors. Our results from the cosine score distribution analysis and assay neighbor clustering analysis on all PubChem bioassays suggest that strong correlations among the bioassays can be identified from their conceptual relevance. A comparison with other existing assay neighboring methods suggests that the text-mining based bioassay neighboring approach provides meaningful linkages among the PubChem bioassays, and complements the existing methods by identifying additional relationships among the bioassay entries. The text-mining based bioassay neighboring analysis is efficient for correlating bioassays and studying different aspects of a biological process, which are otherwise difficult to achieve by existing neighboring procedures due to the lack of specific annotations and structured information. It is suggested that the text-mining based bioassay neighboring analysis can be used as a standalone or as a complementary tool for the PubChem bioassay neighboring process to enable efficient integration of assay results and generate hypotheses for the discovery of bioactivities of the tested reagents.

  2. Beyond accuracy: creating interoperable and scalable text-mining web services.

    Science.gov (United States)

    Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

    2016-06-15

    The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl : Zhiyong.Lu@nih.gov. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

  3. Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

    DEFF Research Database (Denmark)

    Debortoli, Stefan; Müller, Oliver; Junglas, Iris

    2016-01-01

    , such as manual coding. Yet, the size of text data setsobtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challengesencountered when applying automated text-mining techniques in information systems research. In particular, weshowcase the use of probabilistic...... researchers,this tutorial provides some guidance for conducting text mining studies on their own and for evaluating the quality ofothers.......t is estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video);and much of it is expressed in rich and ambiguous natural language. Traditionally, the analysis of natural languagehas prompted the use of qualitative data analysis approaches...

  4. From university research to innovation Detecting knowledge transfer via text mining

    DEFF Research Database (Denmark)

    Woltmann, Sabrina; Clemmensen, Line Katrine Harder; Alkærsig, Lars

    2016-01-01

    and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern...... associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using...... recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent...

  5. A tm Plug-In for Distributed Text Mining in R

    Directory of Open Access Journals (Sweden)

    Stefan Theussl

    2012-11-01

    Full Text Available R has gained explicit text mining support with the tm package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text corpora. However, we typically face two challenges when analyzing large corpora: (1 the amount of data to be processed in a single machine is usually limited by the available main memory (i.e., RAM, and (2 the more data to be analyzed the higher the need for efficient procedures for calculating valuable results. Fortunately, adequate programming models like MapReduce facilitate parallelization of text mining tasks and allow for processing data sets beyond what would fit into memory by using a distributed file system possibly spanning over several machines, e.g., in a cluster of workstations. In this paper we present a plug-in package to tm called tm.plugin.dc implementing a distributed corpus class which can take advantage of the Hadoop MapReduce library for large scale text mining tasks. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.

  6. An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.

    Science.gov (United States)

    Arighi, Cecilia N; Carterette, Ben; Cohen, K Bretonnel; Krallinger, Martin; Wilbur, W John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

    2013-01-01

    In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators' overall experience of a system, regardless of the system's high score on design, learnability and

  7. Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.

    Science.gov (United States)

    Fu, Xiao; Batista-Navarro, Riza; Rak, Rafal; Ananiadou, Sophia

    2015-01-01

    Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often "hidden" within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients. A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents. When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors. We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.

  8. Mining protein function from text using term-based support vector machines

    Science.gov (United States)

    Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

    2005-01-01

    Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

  9. Negative and positive association rules mining from text using frequent and infrequent itemsets.

    Science.gov (United States)

    Mahmood, Sajid; Shahbaz, Muhammad; Guergachi, Aziz

    2014-01-01

    Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of accurate NARs, and their huge number as compared with positive association rules. In medical science, for example, one is interested in factors which can either adjudicate the presence of a disease or write-off of its possibility. The vivid positive symptoms are often obvious; however, negative symptoms are subtler and more difficult to recognize and diagnose. In this paper, we propose an algorithm for discovering positive and negative association rules among frequent and infrequent itemsets. We identify associations among medications, symptoms, and laboratory results using state-of-the-art data mining technology.

  10. Linking genes to literature: text mining, information extraction, and retrieval applications for biology.

    Science.gov (United States)

    Krallinger, Martin; Valencia, Alfonso; Hirschman, Lynette

    2008-01-01

    Efficient access to information contained in online scientific literature collections is essential for life science research, playing a crucial role from the initial stage of experiment planning to the final interpretation and communication of the results. The biological literature also constitutes the main information source for manual literature curation used by expert-curated databases. Following the increasing popularity of web-based applications for analyzing biological data, new text-mining and information extraction strategies are being implemented. These systems exploit existing regularities in natural language to extract biologically relevant information from electronic texts automatically. The aim of the BioCreative challenge is to promote the development of such tools and to provide insight into their performance. This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the following: the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications. The current trend in biomedical text mining points toward an increasing diversification in terms of application types and techniques, together with integration of domain-specific resources such as ontologies. Additional descriptions of some of the systems discussed here are available on the internet http://zope.bioinfo.cnio.es/bionlp_tools/.

  11. Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

    Science.gov (United States)

    Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

    2017-08-01

    Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

  12. Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.

    Science.gov (United States)

    Cormack, James; Nath, Chinmoy; Milward, David; Raja, Kalpana; Jonnalagadda, Siddhartha R

    2015-12-01

    This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

    Science.gov (United States)

    Elayavilli, Ravikumar Komandur; Liu, Hongfang

    2016-01-01

    Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological

  14. Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

    OpenAIRE

    Chen, Hao; McKeever, Susan; Delany, Sarah Jane

    2016-01-01

    Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouT...

  15. Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis

    OpenAIRE

    Alexandra Amado; Paulo Cortez; Paulo Rita; Sérgio Moro

    2018-01-01

    Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The finding...

  16. Experiences with Text Mining Large Collections of Unstructured Systems Development Artifacts at JPL

    Science.gov (United States)

    Port, Dan; Nikora, Allen; Hihn, Jairus; Huang, LiGuo

    2011-01-01

    Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

  17. Coronary artery disease risk assessment from unstructured electronic health records using text mining.

    Science.gov (United States)

    Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Chang, Nai-Wen; Dai, Hong-Jie

    2015-12-01

    Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach.

    Science.gov (United States)

    Abbe, Adeline; Falissard, Bruno

    2017-10-23

    Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics). Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.

  19. The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere)

    Science.gov (United States)

    Znikina, Ludmila; Rozhneva, Elena

    2017-11-01

    The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

  20. HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

    Science.gov (United States)

    Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

    2015-04-01

    The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Automatic extraction of reference gene from literature in plants based on texting mining.

    Science.gov (United States)

    He, Lin; Shen, Gengyu; Li, Fei; Huang, Shuiqing

    2015-01-01

    Real-Time Quantitative Polymerase Chain Reaction (qRT-PCR) is widely used in biological research. It is a key to the availability of qRT-PCR experiment to select a stable reference gene. However, selecting an appropriate reference gene usually requires strict biological experiment for verification with high cost in the process of selection. Scientific literatures have accumulated a lot of achievements on the selection of reference gene. Therefore, mining reference genes under specific experiment environments from literatures can provide quite reliable reference genes for similar qRT-PCR experiments with the advantages of reliability, economic and efficiency. An auxiliary reference gene discovery method from literature is proposed in this paper which integrated machine learning, natural language processing and text mining approaches. The validity tests showed that this new method has a better precision and recall on the extraction of reference genes and their environments.

  2. A Study on Environmental Research Trends Using Text-Mining Method - Focus on Spatial information and ICT -

    Science.gov (United States)

    Lee, M. J.; Oh, K. Y.; Joung-ho, L.

    2016-12-01

    Recently there are many research about analysing the interaction between entities by text-mining analysis in various fields. In this paper, we aimed to quantitatively analyse research-trends in the area of environmental research relating either spatial information or ICT (Information and Communications Technology) by Text-mining analysis. To do this, we applied low-dimensional embedding method, clustering analysis, and association rule to find meaningful associative patterns of key words frequently appeared in the articles. As the authors suppose that KCI (Korea Citation Index) articles reflect academic demands, total 1228 KCI articles that have been published from 1996 to 2015 were reviewed and analysed by Text-mining method. First, we derived KCI articles from NDSL(National Discovery for Science Leaders) site. And then we pre-processed their key-words elected from abstract and then classified those in separable sectors. We investigated the appearance rates and association rule of key-words for articles in the two fields: spatial-information and ICT. In order to detect historic trends, analysis was conducted separately for the four periods: 1996-2000, 2001-2005, 2006-2010, 2011-2015. These analysis were conducted with the usage of R-software. As a result, we conformed that environmental research relating spatial information mainly focused upon such fields as `GIS(35%)', `Remote-Sensing(25%)', `environmental theme map(15.7%)'. Next, `ICT technology(23.6%)', `ICT service(5.4%)', `mobile(24%)', `big data(10%)', `AI(7%)' are primarily emerging from environmental research relating ICT. Thus, from the analysis results, this paper asserts that research trends and academic progresses are well-structured to review recent spatial information and ICT technology and the outcomes of the analysis can be an adequate guidelines to establish environment policies and strategies. KEY WORDS: Big data, Test-mining, Environmental research, Spatial-information, ICT Acknowledgements: The

  3. 76 FR 32951 - Coal Mining Equipment, Technologies and Services Trade Mission to China and Mongolia

    Science.gov (United States)

    2011-06-07

    ... International Trade Administration Coal Mining Equipment, Technologies and Services Trade Mission to China and... operating in the coal and mining sector and manufacturing or distributing mining and mining-safety equipment... as well as appropriate government meetings. The mission will conclude at the China Coal and Mining...

  4. Tracing Knowledge Transfer from Universities to Industry: A Text Mining Approach

    DEFF Research Database (Denmark)

    Woltmann, Sabrina; Alkærsig, Lars

    2017-01-01

    This paper identifies transferred knowledge between universities and the industry by proposing the use of a computational linguistic method. Current research on university-industry knowledge exchange relies often on formal databases and indicators such as patents, collaborative publications and l...... is the first step to enable the identification of common knowledge and knowledge transfer via text mining to increase its measurability....... and license agreements, to assess the contribution to the socioeconomic surrounding of universities. We, on the other hand, use the texts from university abstracts to identify university knowledge and compare them with texts from firm webpages. We use these text data to identify common key words and thereby...... identify overlapping contents among the texts. As method we use a well-established word ranking method from the field of information retrieval term frequency–inverse document frequency (TFIDF) to identify commonalities between texts from university. In examining the outcomes of the TFIDF statistic we find...

  5. Cluo: Web-Scale Text Mining System For Open Source Intelligence Purposes

    Directory of Open Access Journals (Sweden)

    Przemyslaw Maciolek

    2013-01-01

    Full Text Available The amount of textual information published on the Internet is considered tobe in billions of web pages, blog posts, comments, social media updates andothers. Analyzing such quantities of data requires high level of distribution –both data and computing. This is especially true in case of complex algorithms,often used in text mining tasks.The paper presents a prototype implementation of CLUO – an Open SourceIntelligence (OSINT system, which extracts and analyzes significant quantitiesof openly available information.

  6. Remote Mine Detection Technologies for Land and Water Environments

    Energy Technology Data Exchange (ETDEWEB)

    Hoover, Eddie R.

    1999-05-11

    The detection of mines, both during and after hostilities, is a growing international problem. It limits military operations during wartime and unrecovered mines create tragic consequences for civilians. From a purely humanitarian standpoint an estimated 100 million or more unrecovered mines are located in over 60 countries worldwide. This paper presents an overview of some of the technologies currently being investigated by Sandia National Laboratories for the detection and monitoring of minefields in land and water environments. The three technical areas described in this paper are: 1) the development of new mathematical techniques for combining or fusing the data from multiple sources for enhanced decision-making; 2) an environmental fate and transport (EF&T) analysis approach that is central to improving trace chemical sensing technique; and 3) the investigation of an underwater range imaging device to aid in locating and characterizing mines and other obstacles in coastal waters.

  7. Using a Text-Mining Approach to Evaluate the Quality of Nursing Records.

    Science.gov (United States)

    Chang, Hsiu-Mei; Chiou, Shwu-Fen; Liu, Hsiu-Yun; Yu, Hui-Chu

    2016-01-01

    Nursing records in Taiwan have been computerized, but their quality has rarely been discussed. Therefore, this study employed a text-mining approach and a cross-sectional retrospective research design to evaluate the quality of electronic nursing records at a medical center in Northern Taiwan. SAS Text Miner software Version 13.2 was employed to analyze unstructured nursing event records. The results show that SAS Text Miner is suitable for developing a textmining model for validating nursing records. The sensitivity of SAS Text Miner was approximately 0.94, and the specificity and accuracy were 0.99. Thus, SAS Text Miner software is an effective tool for auditing unstructured electronic nursing records.

  8. [Exploring the clinical characters of Shugan Jieyu capsule through text mining].

    Science.gov (United States)

    Pu, Zheng-Ping; Xia, Jiang-Ming; Xie, Wei; He, Jin-Cai

    2017-09-01

    The study was main to explore the clinical characters of Shugan Jieyu capsule through text mining. The data sets of Shugan Jieyu capsule were downloaded from CMCC database by the method of literature retrieved from May 2009 to Jan 2016. Rules of Chinese medical patterns, diseases, symptoms and combination treatment were mined out by data slicing algorithm, and they were demonstrated in frequency tables and two dimension based network. Then totally 190 literature were recruited. The outcomess suggested that SC was most frequently correlated with liver Qi stagnation. Primary depression, depression due to brain disease, concomitant depression followed by physical diseases, concomitant depression followed by schizophrenia and functional dyspepsia were main diseases treated by Shugan Jieyu capsule. Symptoms like low mood, psychic anxiety, somatic anxiety and dysfunction of automatic nerve were mainy relieved bv Shugan Jieyu capsule.For combination treatment. Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. The research suggested that syndrome types and mining results of Shugan Jieyu capsule were almost the same as its instructions. Syndrome of malnutrition of heart spirit was the potential Chinese medical pattern of Shugan Jieyu capsule. Primary comorbid anxiety and depression, concomitant comorbid anxiety and depression followed by physical diseases, and postpartum depression were potential diseases treated by Shugan Jieyu capsule.For combination treatment, Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. Copyright© by the Chinese Pharmaceutical Association.

  9. tmBioC: improving interoperability of text-mining tools with BioC.

    Science.gov (United States)

    Khare, Ritu; Wei, Chih-Hsuan; Mao, Yuqing; Leaman, Robert; Lu, Zhiyong

    2014-01-01

    The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit

  10. Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns.

    Science.gov (United States)

    Park, Jungsik; Ryu, Young Uk

    2014-10-07

    The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). A total of 399 memoirs were collected from an FM group website. The unstructured data of memoirs associated with FM were collected through a crawling process and converted into structured data with a concordance, parts of speech tagging, and word frequency. We also conducted a lexical analysis and phrase pattern identification. After examining the data, a set of FM-related keywords were obtained and phrase net relationships were set through a web-based visualization tool. The clinical distinction of FM was verified. Pain is the biggest issue to the FM patients. The pains were affecting body parts including 'muscles,' 'leg,' 'neck,' 'back,' 'joints,' and 'shoulders' with accompanying symptoms such as 'spasms,' 'stiffness,' and 'aching,' and were described as 'sever,' 'chronic,' and 'constant.' This study also demonstrated that it was possible to understand the interests and concerns of FM patients through text-mining. FM patients wanted to escape from the pain and symptoms, so they were interested in medical treatment and help. Also, they seemed to have interest in their work and occupation, and hope to continue to live life through the relationships with the people around them. This research shows the potential for extracting keywords to confirm the clinical distinction of a certain disease, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. However, it is believed that there are limitations to the processes and methods for organizing and classifying large amounts of text, so these limits have to be considered when analyzing the results. The development of research methodology to overcome these limitations is greatly needed.

  11. Public reactions to e-cigarette regulations on Twitter: a text mining analysis.

    Science.gov (United States)

    Lazard, Allison J; Wilcox, Gary B; Tuttle, Hannah M; Glowacki, Elizabeth M; Pikowski, Jessica

    2017-12-01

    In May 2016, the Food and Drug Administration (FDA) issued a final rule that deemed e-cigarettes to be within their regulatory authority as a tobacco product. News and opinions about the regulation were shared on social media platforms, such as Twitter, which can play an important role in shaping the public's attitudes. We analysed information shared on Twitter for insights into initial public reactions. A text mining approach was used to uncover important topics among reactions to the e-cigarette regulations on Twitter. SAS Text Miner V.12.1 software was used for descriptive text mining to uncover the primary topics from tweets collected from May 1 to May 17 2016 using NUVI software to gather the data. A total of nine topics were generated. These topics reveal initial reactions to whether the FDA's e-cigarette regulations will benefit or harm public health, how the regulations will impact the emerging e-cigarette market and efforts to share the news. The topics were dominated by negative or mixed reactions. In the days following the FDA's announcement of the new deeming regulations, the public reaction on Twitter was largely negative. Public health advocates should consider using social media outlets to better communicate the policy's intentions, reach and potential impact for public good to create a more balanced conversation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  12. Research on surveying technology applied for DTM modelling and volume computation in open pit mines

    Directory of Open Access Journals (Sweden)

    Jaroslaw Wajs

    2016-01-01

    Full Text Available The spatial information systems of mining company can be used for monitoring of mining activity, excavation planning, calculations of the ore volume and decision making. Nowadays, data base has to be updated by sources such as surveying positioning technologies and remote sensed photogrammetry data. The presented paper contains review of the methodology for the digital terrain model, i.e. DTM, modelling and obtaining data from surveying technologies in an open pit mine or quarry. This paper reviews the application of GPS, total station measurements, and ground photogrammetry for the volume accuracy assessment of a selected object. The testing field was situated in Belchatow lignite open pit mine. A suitable object had been selected. The testing layer of coal seam was located at 8’th pit sidewall excavation area. The data were acquired two times within one month period and it was connected with monthly DTM actualization of excavation. This paper presents the technological process and the results of the research of using digital photogrammetry for opencast mining purposes in the scope of numerical volume computation and monitoring the mines by comparison of different sources. The results shows that the presented workflow allow to build DTM manually and remote sensed and the accuracy assessment was presented by the volume computation pathway. Major advantages of the techniques are presented illustrating how a terrestrial photogrammetry techniques provide rapid spatial measurements of breaklines 3D data utilized to volume calculation.

  13. Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

    Science.gov (United States)

    Kilicoglu, Halil

    2017-06-13

    An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise. Published by Oxford University Press 2017. This work is written by a US Government employee and is in the public domain in the US.

  14. Text Mining of the Classical Medical Literature for Medicines That Show Potential in Diabetic Nephropathy

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2014-01-01

    Full Text Available Objectives. To apply modern text-mining methods to identify candidate herbs and formulae for the treatment of diabetic nephropathy. Methods. The method we developed includes three steps: (1 identification of candidate ancient terms; (2 systemic search and assessment of medical records written in classical Chinese; (3 preliminary evaluation of the effect and safety of candidates. Results. Ancient terms Xia Xiao, Shen Xiao, and Xiao Shen were determined as the most likely to correspond with diabetic nephropathy and used in text mining. A total of 80 Chinese formulae for treating conditions congruent with diabetic nephropathy recorded in medical books from Tang Dynasty to Qing Dynasty were collected. Sao si tang (also called Reeling Silk Decoction was chosen to show the process of preliminary evaluation of the candidates. It had promising potential for development as new agent for the treatment of diabetic nephropathy. However, further investigations about the safety to patients with renal insufficiency are still needed. Conclusions. The methods developed in this study offer a targeted approach to identifying traditional herbs and/or formulae as candidates for further investigation in the search for new drugs for modern disease. However, more effort is still required to improve our techniques, especially with regard to compound formulae.

  15. U-Compare: share and compare text mining tools with UIMA

    Science.gov (United States)

    Kano, Yoshinobu; Baumgartner, William A.; McCrohon, Luke; Ananiadou, Sophia; Cohen, K. Bretonnel; Hunter, Lawrence; Tsujii, Jun'ichi

    2009-01-01

    Summary: Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. Availability: http://u-compare.org/ Contact: kano@is.s.u-tokyo.ac.jp PMID:19414535

  16. A MeSH-based text mining method for identifying novel prebiotics.

    Science.gov (United States)

    Shan, Guangyu; Lu, Yiming; Min, Bo; Qu, Wubin; Zhang, Chenggang

    2016-12-01

    Prebiotics contribute to the well-being of their host by altering the composition of the gut microbiota. Discovering new prebiotics is a challenging and arduous task due to strict inclusion criteria; thus, highly limited numbers of prebiotic candidates have been identified. Notably, the large numbers of published studies may contain substantial information attached to various features of known prebiotics that can be used to predict new candidates. In this paper, we propose a medical subject headings (MeSH)-based text mining method for identifying new prebiotics with structured texts obtained from PubMed. We defined an optimal feature set for prebiotics prediction using a systematic feature-ranking algorithm with which a variety of carbohydrates can be accurately classified into different clusters in accordance with their chemical and biological attributes. The optimal feature set was used to separate positive prebiotics from other carbohydrates, and a cross-validation procedure was employed to assess the prediction accuracy of the model. Our method achieved a specificity of 0.876 and a sensitivity of 0.838. Finally, we identified a high-confidence list of candidates of prebiotics that are strongly supported by the literature. Our study demonstrates that text mining from high-volume biomedical literature is a promising approach in searching for new prebiotics.

  17. Recovering ethics after 'technics': developing critical text on technology.

    Science.gov (United States)

    Marck, P B

    2000-01-01

    Much modern science and ethics debate is on high-profile problems such as animal organ transplantation, genetic engineering and fetal tissue research, in discourse that assumes technical tones. Other work, such as narrative ethics, expresses the failed promise of technology in the vivid detail of human experience. However, the essential nature of contemporary technology remains largely opaque to our present ethical lens on health care and on society. The limited controversies of modern science and ethics perpetuate 'technics', a technical, problem-solving mindset that fails to grapple successfully with the complexity of technology. A critical dialectic between practice and scholarship widens the ethical conversation in nursing to consider technology as an ongoing set of daily and fundamental moral choices on how we live. Critical text on technology recovers ethics from the limits of technics, and assists nurses to develop an inherent knowledge of technology that is needed to provide ethical care in a technological world. There are overlooked ethical challenges in the mundane, everyday routine activities of professional practice, and these have gone largely unexamined. Ethical behavior is not the display of one's moral rectitude in times of crisis. It is the day-to-day expression of one's commitment to other persons and the ways in which human beings relate to one another in their daily interactions.

  18. Remediation of contaminated soil using heap leach mining technology

    Energy Technology Data Exchange (ETDEWEB)

    York, D.A.; Aamodt, P.L.

    1990-01-01

    Los Alamos National Laboratory is evaluating the systems technology for heap treatment of excavated soils to remove and treat hazardous chemical and radioactive wastes. This new technology would be an extrapolation of current heap leach mining technology. The candidate wastes for treatment are those organic or inorganic (including radioactive) compounds that will chemically, physically, or biologically react with selected reagents. The project would start with bench-scale testing, followed by pilot-scale testing, and eventually by field-scale testing. Various reagents would be tried in various combinations and sequences to obtain and optimize the desired treatment results. The field-scale testing would be preceded by site characterization, process design, and equipment selection. The final step in this project is to transfer the systems technology to the private sector, probably to the mining industry. 6 refs., 1 fig.

  19. Access to information technology and willingness to receive text ...

    African Journals Online (AJOL)

    Access to information technology and willingness to receive text message reminders for childhood immunisation among mothers attending a tertiary facility in Lagos, Nigeria. ... aged <5 years who brought their children to attend the immuno-prophylaxis and child welfare clinic of Lagos University Teaching Hospital

  20. Technologies required for safe and profitable deep level gold mining, South Africa

    CSIR Research Space (South Africa)

    Willis, PH

    2000-01-01

    Full Text Available Since the 14th CMMI conference, held in Edinburgh in 1990, at which a paper was presented by the author (Willis, 1990) reviewing the role of integrating new technology as a survival strategy for South African gold mines, considerable change has...

  1. Comparison between BIDE, PrefixSpan, and TRuleGrowth for Mining of Indonesian Text

    Science.gov (United States)

    Sa'adillah Maylawati, Dian; Irfan, Mohamad; Budiawan Zulfikar, Wildan

    2017-01-01

    Mining proscess for Indonesian language still be an interesting research. Multiple of words representation was claimed can keep the meaning of text better than bag of words. In this paper, we compare several sequential pattern algortihm, among others BIDE (BIDirectional Extention), PrefixSpan, and TRuleGrowth. All of those algorithm produce frequent word sequence to keep the meaning of text. However, the experiment result, with 14.006 of Indonesian tweet from Twitter, shows that BIDE can produce more efficient frequent word sequence than PrefixSpan and TRuleGrowth without missing the meaning of text. Then, the average of time process of PrefixSpan is faster than BIDE and TRuleGrowth. In the other hand, PrefixSpan and TRuleGrowth is more efficient in using memory than BIDE.

  2. Mining

    Directory of Open Access Journals (Sweden)

    Khairullah Khan

    2014-09-01

    Full Text Available Opinion mining is an interesting area of research because of its applications in various fields. Collecting opinions of people about products and about social and political events and problems through the Web is becoming increasingly popular every day. The opinions of users are helpful for the public and for stakeholders when making certain decisions. Opinion mining is a way to retrieve information through search engines, Web blogs and social networks. Because of the huge number of reviews in the form of unstructured text, it is impossible to summarize the information manually. Accordingly, efficient computational methods are needed for mining and summarizing the reviews from corpuses and Web documents. This study presents a systematic literature survey regarding the computational techniques, models and algorithms for mining opinion components from unstructured reviews.

  3. Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

    Science.gov (United States)

    Tiffin, Nicki; Kelso, Janet F; Powell, Alan R; Pan, Hong; Bajic, Vladimir B; Hide, Winston A

    2005-01-01

    Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

  4. DDMGD: the database of text-mined associations between genes methylated in diseases from different species.

    Science.gov (United States)

    Bin Raies, Arwa; Mansour, Hicham; Incitti, Roberto; Bajic, Vladimir B

    2015-01-01

    Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  5. Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

    Energy Technology Data Exchange (ETDEWEB)

    Hirdt, J.A. [Department of Mathematics and Computer Science, St. Joseph' s College, Patchogue, NY 11772 (United States); Brown, D.A., E-mail: dbrown@bnl.gov [National Nuclear Data Center, Brookhaven National Laboratory, Upton, NY 11973-5000 (United States)

    2016-01-15

    The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

  6. DDMGD: the database of text-mined associations between genes methylated in diseases from different species

    KAUST Repository

    Raies, A. B.

    2014-11-14

    Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD\\'s scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.

  7. Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

    Science.gov (United States)

    Hirdt, J. A.; Brown, D. A.

    2016-01-01

    The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

  8. Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach.

    Science.gov (United States)

    Zhen, Cheng; Zhu, Caizhong; Chen, Haoyang; Xiong, Yiru; Tan, Junyuan; Chen, Dong; Li, Jin

    2017-02-21

    To systematically explore the molecular mechanism for hepatocellular carcinoma (HCC) metastasis and identify regulatory genes with text mining methods. Genes with highest frequencies and significant pathways related to HCC metastasis were listed. A handful of proteins such as EGFR, MDM2, TP53 and APP, were identified as hub nodes in PPI (protein-protein interaction) network. Compared with unique genes for HBV-HCCs, genes particular to HCV-HCCs were less, but may participate in more extensive signaling processes. VEGFA, PI3KCA, MAPK1, MMP9 and other genes may play important roles in multiple phenotypes of metastasis. Genes in abstracts of HCC-metastasis literatures were identified. Word frequency analysis, KEGG pathway and PPI network analysis were performed. Then co-occurrence analysis between genes and metastasis-related phenotypes were carried out. Text mining is effective for revealing potential regulators or pathways, but the purpose of it should be specific, and the combination of various methods will be more useful.

  9. Combining literature text mining with microarray data: advances for system biology modeling.

    Science.gov (United States)

    Faro, Alberto; Giordano, Daniela; Spampinato, Concetto

    2012-01-01

    A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.

  10. BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.

    Science.gov (United States)

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Pafilis, Evangelos; Theodosiou, Theodosios; Schneider, Reinhard; Satagopam, Venkata P; Ouzounis, Christos A; Eliopoulos, Aristides G; Promponas, Vasilis J; Iliopoulos, Ioannis

    2014-11-15

    The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University

  11. From university research to innovation: Detecting knowledge transfer via text mining

    Energy Technology Data Exchange (ETDEWEB)

    Woltmann, S.; Clemmensen, L.; Alkærsig, L

    2016-07-01

    Knowledge transfer by universities is a top priority in innovation policy and a primary purpose for public research funding, due to being an important driver of technical change and innovation. Current empirical research on the impact of university research relies mainly on formal databases and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent text collections (corpora) to identify correlations of university publications and company webpages. One corpus representing the company sites, serving as sample of the private economy and a second corpus, providing the reference to the university research, containing relevant publications. We associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using contemporary techniques for the measurement of public research impact. The approach aims to be applicable across universities and thus enable a more holistic comparable assessment. This rely less on formal databases, which is certainly beneficial in terms of the data reliability. We seek to provide a supplementary perspective for the detection of the dissemination of university research and hereby enable policy makers to gain additional insights of (informal) contributions of knowledge

  12. Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

    Directory of Open Access Journals (Sweden)

    Ayush Singhal

    2016-11-01

    Full Text Available The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed. Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD, diabetes mellitus, and cystic fibrosis. We then evaluate our approach in two ways: (1 a direct comparison with the state of the art using benchmark datasets; (2 a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79 over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB, we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets

  13. Texts and data mining and their possibilities applied to the process of news production

    Directory of Open Access Journals (Sweden)

    Walter Teixeira Lima Jr

    2011-02-01

    Full Text Available The proposal of this essay is to discuss the challenges of representing in a formalist computational process the knowledge which the journalist uses to articulate news values for the purpose of selecting and imposing hierarchy on news. It discusses how to make bridges to emulate this knowledge obtained in an empirical form with the bases of computational science, in the area of storage, recovery and linked to data in a database, which must show the way human brains treat information obtained through their sensorial system. Systemizing and automating part of the journalistic process in a database contributes to eliminating distortions, faults and to applying, in an efficient manner, techniques for Data Mining and/or Texts which, by definition, permit the discovery of nontrivial relations.

  14. Texts and data mining and their possibilities applied to the process of news production

    Directory of Open Access Journals (Sweden)

    Walter Teixeira Lima Jr

    2008-06-01

    Full Text Available The proposal of this essay is to discuss the challenges of representing in a formalist computational process the knowledge which the journalist uses to articulate news values for the purpose of selecting and imposing hierarchy on news. It discusses how to make bridges to emulate this knowledge obtained in an empirical form with the bases of computational science, in the area of storage, recovery and linked to data in a database, which must show the way human brains treat information obtained through their sensorial system. Systemizing and automating part of the journalistic process in a database contributes to eliminating distortions, faults and to applying, in an efficient manner, techniques for Data Mining and/or Texts which, by definition, permit the discovery of nontrivial relations.

  15. Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

    Directory of Open Access Journals (Sweden)

    Lun Li

    2014-01-01

    Full Text Available Associating microRNAs (miRNAs with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification.

  16. Internet of Things in Health Trends Through Bibliometrics and Text Mining.

    Science.gov (United States)

    Konstantinidis, Stathis Th; Billis, Antonis; Wharrad, Heather; Bamidis, Panagiotis D

    2017-01-01

    Recently a new buzzword has slowly but surely emerged, namely the Internet of Things (IoT). The importance of IoT is identified worldwide both by organisations and governments and the scientific community with an incremental number of publications during the last few years. IoT in Health is one of the main pillars of this evolution, but limited research has been performed on future visions and trends. Thus, in this study we investigate the longitudinal trends of Internet of Things in Health through bibliometrics and use of text mining. Seven hundred seventy eight (778) articles were retrieved form The Web of Science database from 1998 to 2016. The publications are grouped into thirty (30) clusters based on abstract text analysis resulting into some eight (8) trends of IoT in Health. Research in this field is obviously obtaining a worldwide character with specific trends, which are worth delineating to be in favour of some areas.

  17. The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

    Science.gov (United States)

    Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J; Inzé, Dirk; Van de Peer, Yves

    2013-03-01

    Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

  18. DiMeX: A Text Mining System for Mutation-Disease Association Extraction.

    Science.gov (United States)

    Mahmood, A S M Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K

    2016-01-01

    The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

  19. Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

    Science.gov (United States)

    Singhal, Ayush; Simmons, Michael; Lu, Zhiyong

    2016-11-01

    The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease

  20. Texting

    Science.gov (United States)

    Tilley, Carol L.

    2009-01-01

    With the increasing ranks of cell phone ownership is an increase in text messaging, or texting. During 2008, more than 2.5 trillion text messages were sent worldwide--that's an average of more than 400 messages for every person on the planet. Although many of the messages teenagers text each day are perhaps nothing more than "how r u?" or "c u…

  1. MET network in PubMed: a text-mined network visualization and curation system.

    Science.gov (United States)

    Dai, Hong-Jie; Su, Chu-Hsien; Lai, Po-Ting; Huang, Ming-Siang; Jonnagaddala, Jitendra; Rose Jue, Toni; Rao, Shruti; Chou, Hui-Jou; Milacic, Marija; Singh, Onkar; Syed-Abdul, Shabbir; Hsu, Wen-Lian

    2016-01-01

    Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET.Database URL: http://btm.tmu.edu.tw/metastasisway. © The Author(s) 2016. Published by Oxford University Press.

  2. The microbiology of passive remediation technologies for mine drainage treatment

    Energy Technology Data Exchange (ETDEWEB)

    Hallberg, K.B.; Rolfe, S.; Johnson, D.B. [University of Wales, Bangor (United Kingdom). School of Biological Sciences

    2004-07-01

    Water draining abandoned mines or mines sites is often acidic and contains relatively high concentrations of dissolved metals and sulfate. Such water (AMD) arises from the oxidative dissolution of sulfidic minerals, such as iron pyrite, exposed by mining activities. As AMD production can occur over a very long time scale, passive remediation systems are becoming increasingly important in the treatment of this wastewater. The passive remediation technology of choice to treat AMD from coal mines and coal spoils is composite wetlands. Using molecular-based techniques the authors have followed, from the start-up phase, the changes in microbial populations of wetlands filled with different compost materials. Three pilot-scale wetlands newly constructed at an abandoned coal mine near Aspatria in Cumberland, UK, were chosen for study. The wetlands were constructed of different substances dried sewage sludge, paper pulp mill waste and a mixture of the two. While differences in the populations of the wetlands were evident initially, the populations converged as the wetlands aged. Microbes found to dominate in the early stages of operation were mainly cellulolytic, while sulfate-reducing microbes were detected in the later stages. The microbial composition data are being compared with those from mature wetlands to gain a fuller understanding of the evolution of microbial populations in compost wetlands. 11 refs., 2 figs., 2 tabs.

  3. The impact of new translation technologies on specialized texts

    Directory of Open Access Journals (Sweden)

    Matthieu LeBlanc

    2016-03-01

    Full Text Available The introduction of translation technologies, especially translation memory software, has had a significant impact on both the translator’s professional practice and the target text itself. Apart from the fact that he or she must translate in a non-linear fashion due to the design of translation memory systems, the translator is now called upon to increase output and, in many cases, recycle what has already been translated by others. As a result, the translator, used to having full control over his or her text, is in some regards losing control over the translation process, which brings him or her to reflect on the quality of the final product and, in turn, on the transformations the field of specialized translation is undergoing. In this paper, I will present the results of an important ethnographic study conducted in three Canadian translation environments. I will focus mostly on the effects translation technologies and newly implemented practices have had on the quality of specialized texts destined for the Canadian market, where most of the specialized texts produced in French are in fact translations. Special attention will be given to the comments made by specialized translators during semi-directed interviews.

  4. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    Science.gov (United States)

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.

  5. Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

    Science.gov (United States)

    Leeper, Nicholas J; Bauer-Mehren, Anna; Iyer, Srinivasan V; Lependu, Paea; Olson, Cliff; Shah, Nigam H

    2013-01-01

    Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF). We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients. This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

  6. Standardized UXO Technology Demonstration Site Mine Grid Scoring Record No. 838

    National Research Council Canada - National Science Library

    Fling, Rick; McClung, Christina; Burch, William; McDonnell, Patrick; Teefy, Dennis

    2007-01-01

    ...) utilizing the APG Standardized UXO Technology Demonstration Site Mine Grid. This Scoring Record was coordinated by Dennis Teefy and the Standardized UXO Technology Demonstration Site Scoring Committee...

  7. Standardized UXO Technology Demonstration Site Mine Grid Scoring Record No. 835

    National Research Council Canada - National Science Library

    Teefy, Dennis; Fling, Rick; McClung, Christina

    2007-01-01

    ...) utilizing the APG Standardized UXO Technology Demonstration Site Mine Grid. This Scoring Record was coordinated by Dennis Teefy and the Standardized UXO Technology Demonstration Site Scoring Committee...

  8. The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

    Science.gov (United States)

    Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

    2013-04-01

    The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for the students' inclusion of words associated with the shape (i.e., phase), orientation and location of the Moon along with words about similarities and differences. Almost all students wrote about shape but fewer wrote about orientation or location. Students infrequently included words about similarities or differences in the same sentence with shape, orientation or location. Similar to studies about children's and adults' lunar misconceptions, it was found that male and female early adolescents also lacked a robust understanding of global lunar patterns.

  9. Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level

    DEFF Research Database (Denmark)

    Jensen, Kasper; Panagiotou, Gianni; Kouskoumvekaki, Irene

    2014-01-01

    , lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently...... occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii......) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research....

  10. The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

    Science.gov (United States)

    Hao, Haijing; Zhang, Kunpeng

    2016-05-10

    Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. We conducted two-fold analyses on the data collected from the "Good Doctor Online" platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. On the "Good Doctor Online" platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical

  11. tmVar: a text mining approach for extracting sequence variants in biomedical literature.

    Science.gov (United States)

    Wei, Chih-Hsuan; Harris, Bethany R; Kao, Hung-Yu; Lu, Zhiyong

    2013-06-01

    Text-mining mutation information from the literature becomes a critical part of the bioinformatics approach for the analysis and interpretation of sequence variations in complex diseases in the post-genomic era. It has also been used for assisting the creation of disease-related mutation databases. Most of existing approaches are rule-based and focus on limited types of sequence variations, such as protein point mutations. Thus, extending their extraction scope requires significant manual efforts in examining new instances and developing corresponding rules. As such, new automatic approaches are greatly needed for extracting different kinds of mutations with high accuracy. Here, we report tmVar, a text-mining approach based on conditional random field (CRF) for extracting a wide range of sequence variants described at protein, DNA and RNA levels according to a standard nomenclature developed by the Human Genome Variation Society. By doing so, we cover several important types of mutations that were not considered in past studies. Using a novel CRF label model and feature set, our method achieves higher performance than a state-of-the-art method on both our corpus (91.4 versus 78.1% in F-measure) and their own gold standard (93.9 versus 89.4% in F-measure). These results suggest that tmVar is a high-performance method for mutation extraction from biomedical literature. tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar

  12. Assertions of Japanese Websites for and Against Cancer Screening: a Text Mining Analysis

    Science.gov (United States)

    Okuhara, Tsuyoshi; Ishikawa, Hirono; Okada, Masahumi; Kato, Mio; Kiuchi, Takahiro

    2017-04-01

    Background: Cancer screening rates are lower in Japan than in Western countries such as the United States and the United Kingdom. While health professionals publish pro-cancer-screening messages online to encourage proactive seeking for screening, anti-screening activists use the same medium to warn readers against following guidelines. Contents of pro- and anti-cancer-screening sites may contribute to readers’ acceptance of one or the other position. We aimed to use a text-mining method to examine frequently appearing contents on sites for and against cancer screening. Methods: We conducted online searches in December 2016 using two major search engines in Japan (Google Japan and Yahoo! Japan). Targeted websites were classified as “pro”, “anti”, or “neutral” depending on their claims, with the author(s) classified as “health professional”, “mass media”, or “layperson”. Text-mining analyses were conducted, and statistical analysis was performed using the chi-square test. Results: Of the 169 websites analyzed, the top-three most frequently appearing content topics in pro sites were reducing mortality via cancer screening, benefits of early detection, and recommendations for obtaining detailed examination. The top three most frequent in anti-sites were harm from radiation exposure, non-efficacy of cancer screening, and lack of necessity of early detection. Anti-sites also frequently referred to a well-known Japanese radiologist, Makoto Kondo, who rejects the standard forms of cancer care. Conclusion: Our findings should enable authors of pro-cancer-screening sites to write to counter misleading anti-cancer-screening messages and facilitate dissemination of accurate information. Creative Commons Attribution License

  13. Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist.

    Science.gov (United States)

    Olorisade, Babatunde Kazeem; Brereton, Pearl; Andras, Peter

    2017-09-01

    Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields. However, there is little work so far on reproduction studies in the field. In this paper, we investigate the reproducibility of studies in this area based on information contained in published articles and we propose reporting guidelines that could improve reproducibility. The study was approached in two ways. Initially we attempted to reproduce results from six studies, which were based on the same raw dataset. Then, based on this experience, we identified steps considered essential to successful reproduction of text mining experiments and characterized them to measure how reproducible is a study given the information provided on these steps. 33 articles were systematically assessed for reproducibility using this approach. Our work revealed that it is currently difficult if not impossible to independently reproduce the results published in any of the studies investigated. The lack of information about the datasets used limits reproducibility of about 80% of the studies assessed. Also, information about the machine learning algorithms is inadequate in about 27% of the papers. On the plus side, the third party software tools used are mostly free and available. The reproducibility potential of most of the studies can be significantly improved if more attention is paid to information provided on the datasets used, how they were partitioned and utilized, and

  14. The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere

    Directory of Open Access Journals (Sweden)

    Znikina Ludmila

    2017-01-01

    Full Text Available The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

  15. Sustainable Supply Chain Based on News Articles and Sustainability Reports: Text Mining with Leximancer and DICTION

    Directory of Open Access Journals (Sweden)

    Dongwook Kim

    2017-06-01

    Full Text Available The purpose of this research is to explore sustainable supply chain management (SSCM trends, and firms’ strategic positioning and execution with regard to sustainability in the textile and apparel industry based on news articles and sustainability reports. Further analysis of the rhetoric in Chief executive officer (CEO letters within sustainability reports is used to determine firms’ resoluteness, positive entailments, sharing of values, perception of reality, and sustainability strategy and execution feasibility. Computer-based content analysis is used for this research: Leximancer is applied for text analysis, while dictionary-based text mining program DICTION and SPSS are used for rhetorical analysis. Overall, contents similar to the literature on environmental, social, and economic aspects of the triple bottom line (TBL are observed, however, topics such as regulation, green incentives, and international standards are not readily observed. Furthmore, ethical issues, sustainable production, quality, and customer roles are emphasized in texts analyzed. The CEO letter analysis indicates that listed firms show relatively low realism and high commonality, while North American firms exhibit relatively high commonality, and Europe firms show relatively high realism. The results will serve as a baseline for providing academia guidelines in SSCM research, and provide an opportunity for businesses to complement their sustainability strategies and executions.

  16. Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

    Directory of Open Access Journals (Sweden)

    Nicholas J Leeper

    Full Text Available BACKGROUND: Peripheral arterial disease (PAD is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF. METHODS AND RESULTS: We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55], myocardial infarction (OR = 1.00, CI [0.71, 1.39], or death (OR = 0.86, CI [0.63, 1.18]. Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients. CONCLUSIONS: This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

  17. Development of Workshops on Biodiversity and Evaluation of the Educational Effect by Text Mining Analysis

    Science.gov (United States)

    Baba, R.; Iijima, A.

    2014-12-01

    Conservation of biodiversity is one of the key issues in the environmental studies. As means to solve this issue, education is becoming increasingly important. In the previous work, we have developed a course of workshops on the conservation of biodiversity. To disseminate the course as a tool for environmental education, determination of the educational effect is essential. A text mining enables analyses of frequency and co-occurrence of words in the freely described texts. This study is intended to evaluate the effect of workshop by using text mining technique. We hosted the originally developed workshop on the conservation of biodiversity for 22 college students. The aim of the workshop was to inform the definition of biodiversity. Generally, biodiversity refers to the diversity of ecosystem, diversity between species, and diversity within species. To facilitate discussion, supplementary materials were used. For instance, field guides of wildlife species were used to discuss about the diversity of ecosystem. Moreover, a hierarchical framework in an ecological pyramid was shown for understanding the role of diversity between species. Besides, we offered a document material on the historical affair of Potato Famine in Ireland to discuss about the diversity within species from the genetic viewpoint. Before and after the workshop, we asked students for free description on the definition of biodiversity, and analyzed by using Tiny Text Miner. This technique enables Japanese language morphological analysis. Frequently-used words were sorted into some categories. Moreover, a principle component analysis was carried out. After the workshop, frequency of the words tagged to diversity between species and diversity within species has significantly increased. From a principle component analysis, the 1st component consists of the words such as producer, consumer, decomposer, and food chain. This indicates that the students have comprehended the close relationship between

  18. Argo: an integrative, interactive, text mining-based workbench supporting curation

    Science.gov (United States)

    Rak, Rafal; Rowley, Andrew; Black, William; Ananiadou, Sophia

    2012-01-01

    Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in

  19. Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain

    Directory of Open Access Journals (Sweden)

    André SANTOS

    2012-07-01

    Full Text Available Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents.

  20. Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain

    Directory of Open Access Journals (Sweden)

    Anália LOURENÇO

    2013-07-01

    Full Text Available Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents.

  1. LiverCancerMarkerRIF: a liver cancer biomarker interactive curation system combining text mining and expert annotations.

    Science.gov (United States)

    Dai, Hong-Jie; Wu, Johnny Chi-Yang; Lin, Wei-San; Reyes, Aaron James F; Dela Rosa, Mira Anne C; Syed-Abdul, Shabbir; Tsai, Richard Tzong-Han; Hsu, Wen-Lian

    2014-01-01

    Biomarkers are biomolecules in the human body that can indicate disease states and abnormal biological processes. Biomarkers are often used during clinical trials to identify patients with cancers. Although biomedical research related to biomarkers has increased over the years and substantial effort has been expended to obtain results in these studies, the specific results obtained often contain ambiguities, and the results might contradict each other. Therefore, the information gathered from these studies must be appropriately integrated and organized to facilitate experimentation on biomarkers. In this study, we used liver cancer as the target and developed a text-mining-based curation system named LiverCancerMarkerRIF, which allows users to retrieve biomarker-related narrations and curators to curate supporting evidence on liver cancer biomarkers directly while browsing PubMed. In contrast to most of the other curation tools that require curators to navigate away from PubMed and accommodate distinct user interfaces or Web sites to complete the curation process, our system provides a user-friendly method for accessing text-mining-aided information and a concise interface to assist curators while they remain at the PubMed Web site. Biomedical text-mining techniques are applied to automatically recognize biomedical concepts such as genes, microRNA, diseases and investigative technologies, which can be used to evaluate the potential of a certain gene as a biomarker. Through the participation in the BioCreative IV user-interactive task, we examined the feasibility of using this novel type of augmented browsing-based curation method, and collaborated with curators to curate biomarker evidential sentences related to liver cancer. The positive feedback received from curators indicates that the proposed method can be effectively used for curation. A publicly available online database containing all the aforementioned information has been constructed at http

  2. Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track

    Science.gov (United States)

    2015-11-20

    Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track Paul N. Bennett Microsoft Research Redmond, USA pauben...investigated the effectiveness of mining session co-occurrence data. For a search engine log, session bound- aries can be defined in the typical way but to...of common failures. To be conservative and attempt to eliminate these failures, we require a candi- date to have overlap with the filter phrase for a

  3. Key Issue: Text mining, copyright and the benefits and barriers to innovation

    Directory of Open Access Journals (Sweden)

    Torsten Reimer

    2012-07-01

    Full Text Available Do you want to cure cancer? It doesn’t matter whether your research is about solving one of the grand challenges of humanity or addressing a more humble question – your first step is likely to be looking at what others have done before. Due to the ever-increasing number of scholarly publications (about 1.5 million new articles published every year, building up an overview of any field of study is an extremely time-consuming process. In prominent topics such as cancer research, it is even more difficult: for the last ten years alone, the UK PubMed Central (UKPMC database lists 312,308 citations with the word ‘cancer’ in the title – browsing them at the leisurely pace of 85 per day will take you about ten years. And by that time, ten years’ worth of new articles on cancer will have appeared. To make such a search even more complex, relevant articles may not feature the keyword ‘cancer’ and critical information may be hiding in a footnote within a completely unrelated publication. There is huge potential for advancing knowledge by systematically identifying, analysing and cross-referencing existing research, but the work required is prohibitively time-consuming and expensive. Unless we use machines to help us – and that is where text mining comes into play.

  4. Role of text mining in early identification of potential drug safety issues.

    Science.gov (United States)

    Liu, Mei; Hu, Yong; Tang, Buzhou

    2014-01-01

    Drugs are an important part of today's medicine, designed to treat, control, and prevent diseases; however, besides their therapeutic effects, drugs may also cause adverse effects that range from cosmetic to severe morbidity and mortality. To identify these potential drug safety issues early, surveillance must be conducted for each drug throughout its life cycle, from drug development to different phases of clinical trials, and continued after market approval. A major aim of pharmacovigilance is to identify the potential drug-event associations that may be novel in nature, severity, and/or frequency. Currently, the state-of-the-art approach for signal detection is through automated procedures by analyzing vast quantities of data for clinical knowledge. There exists a variety of resources for the task, and many of them are textual data that require text analytics and natural language processing to derive high-quality information. This chapter focuses on the utilization of text mining techniques in identifying potential safety issues of drugs from textual sources such as biomedical literature, consumer posts in social media, and narrative electronic medical records.

  5. An unsupervised text mining method for relation extraction from biomedical literature.

    Directory of Open Access Journals (Sweden)

    Changqin Quan

    Full Text Available The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1 Protein-protein interactions extraction, and (2 Gene-suicide association extraction. The evaluation of task (1 on the benchmark dataset (AImed corpus showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

  6. Text mining for identifying topics in the literatures about adolescent substance use and depression

    Directory of Open Access Journals (Sweden)

    Shi-Heng Wang

    2016-03-01

    Full Text Available Abstract Background Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents. Methods In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic. Results The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test. Conclusions Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.

  7. Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

    Science.gov (United States)

    2015-01-01

    Background Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. Methods To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Results Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. Conclusions PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our

  8. Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

    Science.gov (United States)

    Alnazzawi, Noha; Thompson, Paul; Batista-Navarro, Riza; Ananiadou, Sophia

    2015-01-01

    Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single

  9. Mining texts to efficiently generate global data on political regime types

    Directory of Open Access Journals (Sweden)

    Shahryar Minhas

    2015-07-01

    Full Text Available We describe the design and results of an experiment in using text-mining and machine-learning techniques to generate annual measures of national political regime types. Valid and reliable measures of countries’ forms of national government are essential to cross-national and dynamic analysis of many phenomena of great interest to political scientists, including civil war, interstate war, democratization, and coups d’état. Unfortunately, traditional measures of regime type are very expensive to produce, and observations for ambiguous cases are often sharply contested. In this project, we train a series of support vector machine (SVM classifiers to infer regime type from textual data sources. To train the classifiers, we used vectorized textual reports from Freedom House and the State Department as features for a training set of prelabeled regime type data. To validate our SVM classifiers, we compare their predictions in an out-of-sample context, and the performance results across a variety of metrics (accuracy, precision, recall are very high. The results of this project highlight the ability of these techniques to contribute to producing real-time data sources for use in political science that can also be routinely updated at much lower cost than human-coded data. To this end, we set up a text-processing pipeline that pulls updated textual data from selected sources, conducts feature extraction, and applies supervised machine learning methods to produce measures of regime type. This pipeline, written in Python, can be pulled from the Github repository associated with this project and easily extended as more data becomes available.

  10. A practical application of text mining to literature on cognitive rehabilitation and enhancement through neurostimulation

    Directory of Open Access Journals (Sweden)

    Puiu F Balan

    2014-09-01

    Full Text Available The exponential growth in publications represents a major challenge for researchers. Many scientific domains, including neuroscience, are not yet fully engaged in exploiting large bodies of publications. In this paper, we promote the idea to partially automate the processing of scientific documents, specifically using text mining (TM, to efficiently review big corpora of publications. The cognitive advantage given by TM is mainly related to the automatic extraction of relevant trends from corpora of literature, otherwise impossible to analyze in short periods of time. Specifically, the benefits of TM are increased speed, quality and reproducibility of text processing, boosted by rapid updates of the results. First, we selected a set of TM-tools that allow user-friendly approaches of the scientific literature, and which could serve as a guide for researchers willing to incorporate TM in their work. Second, we used these TM-tools to obtain basic insights into the relevant literature on cognitive rehabilitation (CR and cognitive enhancement (CE using transcranial magnetic stimulation (TMS. TM readily extracted the diversity of TMS applications in CR and CE from vast corpora of publications, automatically retrieving trends already described in published reviews. TMS emerged as one of the important non-invasive tools that can both improve cognitive and motor functions in numerous neurological diseases and induce modulations/enhancements of many fundamental brain functions. TM also revealed trends in big corpora of publications by extracting occurrence frequency and relationships of particular subtopics. Moreover, we showed that CR and CE share research topics, both aiming to increase the brain’s capacity to process information, thus supporting their integration in a larger perspective. Methodologically, despite limitations of a simple user-friendly approach, TM served well the reviewing process.

  11. A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

    Science.gov (United States)

    Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

    2013-01-01

    Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

  12. Contents of Japanese pro- and anti-HPV vaccination websites: A text mining analysis.

    Science.gov (United States)

    Okuhara, Tsuyoshi; Ishikawa, Hirono; Okada, Masahumi; Kato, Mio; Kiuchi, Takahiro

    2017-09-23

    In Japan, the human papillomavirus (HPV) vaccination rate has sharply fallen to nearly 0% due to sensational media reports of adverse events. Online anti-HPV-vaccination activists often warn readers of the vaccine's dangers. Here, we aimed to examine frequently appearing contents on pro- and anti-HPV vaccination websites. We conducted online searches via two major search engines (Google Japan and Yahoo! Japan). Targeted websites were classified as "pro," "anti," or "neutral" according to their claims, with the author(s) classified as "health professionals," "mass media," or "laypersons." We then conducted a text mining analysis. Of the 270 sites analyzed, 16 contents were identified. The most frequently appearing contents on pro websites were vaccine side effects, preventable effect of vaccination, and cause of cervical cancer. The most frequently appearing contents on anti websites were vaccine side effects, vaccine toxicity, and girls who suffer from vaccine side effects. Main disseminators of each content according to the author's expertise were also revealed. Pro-HPV vaccination websites should supplement deficient contents and respond to frequent contents on anti-HPV websites. Effective tactics are needed to better communicate susceptibility to cervical cancer, frequency of side effects, and responses to vaccine toxicity and conspiracy theories. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level

    Science.gov (United States)

    Jensen, Kasper; Panagiotou, Gianni; Kouskoumvekaki, Irene

    2014-01-01

    Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research. PMID:24453957

  14. Text mining describes the use of statistical and epidemiological methods in published medical research.

    Science.gov (United States)

    Meaney, Christopher; Moineddin, Rahim; Voruganti, Teja; O'Brien, Mary Ann; Krueger, Paul; Sullivan, Frank

    2016-06-01

    To describe trends in the use of statistical and epidemiological methods in the medical literature over the past 2 decades. We obtained all 1,028,786 articles from the PubMed Central Open-Access archive (retrieved May 9, 2015). We focused on 113,450 medical research articles. A Delphi panel identified 177 statistical/epidemiological methods pertinent to clinical researchers. We used a text-mining approach to determine if a specific statistical/epidemiological method was encountered in a given article. We report the proportion of articles using a specific method for the entire cross-sectional sample and also stratified into three blocks of time (1995-2005; 2006-2010; 2011-2015). Numeric descriptive statistics were commonplace (96.4% articles). Other frequently encountered methods groups included statistical inferential concepts (52.9% articles), epidemiological measures of association (53.5% articles) methods for diagnostic/classification accuracy (40.1% articles), hypothesis testing (28.8% articles), ANOVA (23.2% articles), and regression (22.6% articles). We observed relative percent increases in the use of: regression (103.0%), missing data methods (217.9%), survival analysis (147.6%), and correlated data analysis (192.2%). This study identified commonly encountered and emergent methods used to investigate medical research problems. Clinical researchers must be aware of the methodological landscape in their field, as statistical/epidemiological methods underpin research claims. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Community challenges in biomedical text mining over 10 years: success, failure and the future.

    Science.gov (United States)

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

    One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.

  16. Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics

    OpenAIRE

    Torii, Manabu; Tilak, Sameer S.; Doan, Son; Zisook, Daniel S.; Fan, Jung-Wei

    2016-01-01

    In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the ...

  17. The Machine in the Text: Technology in Introductory Mass Communication Texts.

    Science.gov (United States)

    Jowett, Garth S.

    1984-01-01

    Reviews how the subject of communications technology is treated in nine mass media textbooks that have been adopted widely and form the intellectual introduction for students studying mass communication. (PD)

  18. THE EPA/DOE MINE WASTE TECHNOLOGY PROGRAM

    Science.gov (United States)

    Mining activities in the US (not counting coal) produce between 1-2B tons of mine waste annually. Since many of the ore mines involve sulfide minerals, the production of acid mine drainage (AMD) is a common problem from these abandoned mine sites. The combination of acidity, heav...

  19. BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

    Directory of Open Access Journals (Sweden)

    Tsafnat Guy

    2011-04-01

    Full Text Available Abstract Background The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP to help experts screen drugs that may have important clinical characteristics of interest. Results BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH and the PharmacoKinetic Interaction Screening (PKIS database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100% and 157 (of 197 minor drug classes (80% with areas under the receiver operating characteristic curve (AUC > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238 adverse events (73%, up to 12 (of 15 groups of clinically significant cytochrome P450 enzyme (CYP inducers or inhibitors (80%, and up to 11 (of 14 groups of narrow therapeutic index drugs (79%. Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task. Conclusions BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.

  20. The BioLexicon: a large-scale terminological resource for biomedical text mining

    Directory of Open Access Journals (Sweden)

    Thompson Paul

    2011-10-01

    Full Text Available Abstract Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is

  1. Idea Technology and Product Technology: Seeing beyond the Text to the Technology That Works

    Science.gov (United States)

    Bednar, Maryanne R.

    2004-01-01

    Sifting through the myriad "idea" technologies (such as multiple intelligence theories or Piaget's Theory of Cognitive Development) and "product" technologies (such as PowerPoint or digital cameras) can be overwhelming, but Bednar persuades us that it's not about having the most recent technology, it's about using what works for "your" students in…

  2. Text mining for neuroanatomy using WhiteText with an updated corpus and a new web application

    Directory of Open Access Journals (Sweden)

    Leon eFrench

    2015-05-01

    Full Text Available We describe the WhiteText project, and its progress towards automatically extracting statements of neuroanatomical connectivity from text. We review progress to date on the three main steps of the project: recognition of brain region mentions, standardization of brain region mentions to neuroanatomical nomenclature, and connectivity statement extraction. We further describe a new version of our manually curated corpus that adds 2,111 connectivity statements from 1,828 additional abstracts. Cross-validation classification within the new corpus replicates results on our original corpus, recalling 51% of connectivity statements at 67% precision. The resulting merged corpus provides 5,208 connectivity statements that can be used to seed species-specific connectivity matrices and to better train automated techniques. Finally, we present a new web application that allows fast interactive browsing of the over 70,000 sentences indexed by the system, as a tool for accessing the data and assisting in further curation. Software and data are freely available at http://www.chibi.ubc.ca/WhiteText/.

  3. The Feasibility of Using Large-Scale Text Mining to Detect Adverse Childhood Experiences in a VA-Treated Population.

    Science.gov (United States)

    Hammond, Kenric W; Ben-Ari, Alon Y; Laundry, Ryan J; Boyko, Edward J; Samore, Matthew H

    2015-12-01

    Free text in electronic health records resists large-scale analysis. Text records facts of interest not found in encoded data, and text mining enables their retrieval and quantification. The U.S. Department of Veterans Affairs (VA) clinical data repository affords an opportunity to apply text-mining methodology to study clinical questions in large populations. To assess the feasibility of text mining, investigation of the relationship between exposure to adverse childhood experiences (ACEs) and recorded diagnoses was conducted among all VA-treated Gulf war veterans, utilizing all progress notes recorded from 2000-2011. Text processing extracted ACE exposures recorded among 44.7 million clinical notes belonging to 243,973 veterans. The relationship of ACE exposure to adult illnesses was analyzed using logistic regression. Bias considerations were assessed. ACE score was strongly associated with suicide attempts and serious mental disorders (ORs = 1.84 to 1.97), and less so with behaviorally mediated and somatic conditions (ORs = 1.02 to 1.36) per unit. Bias adjustments did not remove persistent associations between ACE score and most illnesses. Text mining to detect ACE exposure in a large population was feasible. Analysis of the relationship between ACE score and adult health conditions yielded patterns of association consistent with prior research. Copyright © 2015 International Society for Traumatic Stress Studies.

  4. The BioLexicon: a large-scale terminological resource for biomedical text mining

    Science.gov (United States)

    2011-01-01

    Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical

  5. Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

    Science.gov (United States)

    Small, Aeron M; Kiss, Daniel H; Zlatsin, Yevgeny; Birtwell, David L; Williams, Heather; Guerraty, Marie A; Han, Yuchi; Anwaruddin, Saif; Holmes, John H; Chirinos, Julio A; Wilensky, Robert L; Giri, Jay; Rader, Daniel J

    2017-08-01

    Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. These results highlight the superiority of text mining algorithms applied to electronic

  6. Information and communication technology and climate change adaptation: Evidence from selected mining companies in South Africa

    Directory of Open Access Journals (Sweden)

    Bartholomew I. Aleke

    2016-03-01

    Full Text Available The mining sector is a significant contributor to the gross domestic product of many global economies. Given the increasing trends in climate-induced disasters and the growing desire to find lasting solutions, information and communication technology (ICT has been introduced into the climate change adaptation mix. Climate change-induced extreme weather events such as flooding, drought, excessive fog, and cyclones have compounded the environmental challenges faced by the mining sector. This article presents the adoption of ICT innovation as part of the adaptation strategies towards reducing the mining sector’s vulnerability and exposure to climate change disaster risks. Document analysis and systematic literature review were adopted as the methodology. Findings from the study reflect how ICT intervention orchestrated changes in communication patterns which are tailored towards the reduction in climate change vulnerability and exposure. The research concludes with a proposition that ICT intervention must be part of the bigger and ongoing climate change adaptation agenda in the mining sector.Keywords: ICT; climate change; disaster risk reduction; mining; adaptation; South Africa

  7. An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

    Science.gov (United States)

    Scheele, C. J.; Huang, Q.

    2016-12-01

    In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.

  8. Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.

    Science.gov (United States)

    Simmons, Michael; Singhal, Ayush; Lu, Zhiyong

    2016-01-01

    The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

  9. Text Mining to inform construction of Earth and Environmental Science Ontologies

    Science.gov (United States)

    Schildhauer, M.; Adams, B.; Rebich Hespanha, S.

    2013-12-01

    There is a clear need for better semantic representation of Earth and environmental concepts, to facilitate more effective discovery and re-use of information resources relevant to scientists doing integrative research. In order to develop general-purpose Earth and environmental science ontologies, however, it is necessary to represent concepts and relationships that span usage across multiple disciplines and scientific specialties. Traditional knowledge modeling through ontologies utilizes expert knowledge but inevitably favors the particular perspectives of the ontology engineers, as well as the domain experts who interacted with them. This often leads to ontologies that lack robust coverage of synonymy, while also missing important relationships among concepts that can be extremely useful for working scientists to be aware of. In this presentation we will discuss methods we have developed that utilize statistical topic modeling on a large corpus of Earth and environmental science articles, to expand coverage and disclose relationships among concepts in the Earth sciences. For our work we collected a corpus of over 121,000 abstracts from many of the top Earth and environmental science journals. We performed latent Dirichlet allocation topic modeling on this corpus to discover a set of latent topics, which consist of terms that commonly co-occur in abstracts. We match terms in the topics to concept labels in existing ontologies to reveal gaps, and we examine which terms are commonly associated in natural language discourse, to identify relationships that are important to formally model in ontologies. Our text mining methodology uncovers significant gaps in the content of some popular existing ontologies, and we show how, through a workflow involving human interpretation of topic models, we can bootstrap ontologies to have much better coverage and richer semantics. Because we base our methods directly on what working scientists are communicating about their

  10. GIS-technologies for integrated assessment of the productive mining areas

    Energy Technology Data Exchange (ETDEWEB)

    Zamaraev, R.Y.; Oparin, V.N.; Popov, S.E.; Potapov, V.P.; Pyastunovich,O.L. [Russian Academy of Sciences, Kemerovo (Russian Federation)

    2008-05-15

    The paper describes the bases of a new application of GIS-technologies for integrated assessment and comparison of the productive mining areas, involving a wide range of mining and technological factors, considering mineral properties, mineral occurrence conditions and geographical advantages of a mineral deposit location. The model capabilities are exemplified by a comparison of technological characteristics of coals, transportation and power supply infrastructure of the productive mining areas at the Kuznetsk Coal Basin.

  11. The potential of acidophilic macroalgae as part of passive bioremediation technology for acid mine drainage in constructed wetlands

    CSIR Research Space (South Africa)

    Cheng, Po-Hsun

    2012-10-01

    Full Text Available loads in algae exposed to AMD generated from gold and coal mining activities under different environmental conditions. The data generated from this study will be used to inform passive treatment technologies. MATERIALS AND METHODS RESULTS Table 1...- sectional area (m2) Site 1: Klip stream S 25? 37.290? Mud/sediment Decanting surface flows from coal mines Mixture of AMD (coal mining) and untreated sewage effluent vegetated 5?7 E 29? 12.752? Site 2: Brug stream S 25? 51.424? Mud...

  12. Technological and Profitable Analysis of Airlifting in Deep Sea Mining Systems

    Directory of Open Access Journals (Sweden)

    Wenbin Ma

    2017-08-01

    Full Text Available Airlifting technology utilized in deep-sea mining (DSM industry was proposed in the 70s of last century, which was triggered by the discovery of vast amounts of mineral resources on the seabed. The objective of this paper is to assess the technological feasibility and profitability analyses in terms of solid production rate, energy consumption per tonnage of mineral, and profitability per tonnage of mineral. The effects of submergence ratio, pipe diameter, particle diameter, mining depth, and gas flux rate are investigated. The analysis is based on a numerical calculation performed in a Matlab environment. The research reported in this paper can assist to select an optimal transport plan for DSM projects depending on its solid production rate, energy consumption, and profitability.

  13. Text mining of cancer-related information: review of current status and future directions.

    Science.gov (United States)

    Spasić, Irena; Livsey, Jacqueline; Keane, John A; Nenadić, Goran

    2014-09-01

    This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research. A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar. A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main

  14. Unblocking Blockbusters: Using Boolean Text-Mining to Optimise Clinical Trial Design and Timeline for Novel Anticancer Drugs

    Directory of Open Access Journals (Sweden)

    Richard J. Epstein

    2009-08-01

    Full Text Available Two problems now threaten the future of anticancer drug development: (i the information explosion has made research into new target-specific drugs more duplication-prone, and hence less cost-efficient; and (ii high-throughput genomic technologies have failed to deliver the anticipated early windfall of novel first-in-class drugs. Here it is argued that the resulting crisis of blockbuster drug development may be remedied in part by innovative exploitation of informatic power. Using scenarios relating to oncology, it is shown that rapid data-mining of the scientific literature can refine therapeutic hypotheses and thus reduce empirical reliance on preclinical model development and early-phase clinical trials. Moreover, as personalised medicine evolves, this approach may inform biomarker-guided phase III trial strategies for noncytotoxic (antimetastatic drugs that prolong patient survival without necessarily inducing tumor shrinkage. Though not replacing conventional gold standards, these findings suggest that this computational research approach could reduce costly ‘blue skies’ R&D investment and time to market for new biological drugs, thereby helping to reverse unsustainable drug price inflation.

  15. Unblocking Blockbusters: Using Boolean Text-Mining to Optimise Clinical Trial Design and Timeline for Novel Anticancer Drugs

    Directory of Open Access Journals (Sweden)

    Richard J. Epstein

    2009-01-01

    Full Text Available Two problems now threaten the future of anticancer drug development: (i the information explosion has made research into new target-specific drugs more duplication-prone, and hence less cost-efficient; and (ii high-throughput genomic technologies have failed to deliver the anticipated early windfall of novel first-in-class drugs. Here it is argued that the resulting crisis of blockbuster drug development may be remedied in part by innovative exploitation of informatic power. Using scenarios relating to oncology, it is shown that rapid data-mining of the scientific literature can refine therapeutic hypotheses and thus reduce empirical reliance on preclinical model development and early-phase clinical trials. Moreover, as personalised medicine evolves, this approach may inform biomarker-guided phase III trial strategies for noncytotoxic (antimetastatic drugs that prolong patient survival without necessarily inducing tumor shrinkage. Though not replacing conventional gold standards, these findings suggest that this computational research approach could reduce costly ‘blue skies’ R&D investment and time to market for new biological drugs, thereby helping to reverse unsustainable drug price inflation.

  16. Hemodialysis Key Features Mining and Patients Clustering Technologies

    Directory of Open Access Journals (Sweden)

    Tzu-Chuen Lu

    2012-01-01

    Full Text Available The kidneys are very vital organs. Failing kidneys lose their ability to filter out waste products, resulting in kidney disease. To extend or save the lives of patients with impaired kidney function, kidney replacement is typically utilized, such as hemodialysis. This work uses an entropy function to identify key features related to hemodialysis. By identifying these key features, one can determine whether a patient requires hemodialysis. This work uses these key features as dimensions in cluster analysis. The key features can effectively determine whether a patient requires hemodialysis. The proposed data mining scheme finds association rules of each cluster. Hidden rules for causing any kidney disease can therefore be identified. The contributions and key points of this paper are as follows. (1 This paper finds some key features that can be used to predict the patient who may has high probability to perform hemodialysis. (2 The proposed scheme applies k-means clustering algorithm with the key features to category the patients. (3 A data mining technique is used to find the association rules from each cluster. (4 The mined rules can be used to determine whether a patient requires hemodialysis.

  17. Automated assessment of patients' self-narratives for posttraumatic stress disorder screening using natural language processing and text mining

    NARCIS (Netherlands)

    He, Qiwei; Veldkamp, Bernard P.; Glas, Cornelis A.W.; de Vries, Theo

    2017-01-01

    Patients’ narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four

  18. Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

    Science.gov (United States)

    Hung, Jui-Long; Zhang, Ke

    2012-01-01

    This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

  19. Impact of Text-Mining and Imitating Strategies on Lexical Richness, Lexical Diversity and General Success in Second Language Writing

    Science.gov (United States)

    Çepni, Sevcan Bayraktar; Demirel, Elif Tokdemir

    2016-01-01

    This study aimed to find out the impact of "text mining and imitating" strategies on lexical richness, lexical diversity and general success of students in their compositions in second language writing. The participants were 98 students studying their first year in Karadeniz Technical University in English Language and Literature…

  20. Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens

    Directory of Open Access Journals (Sweden)

    Perna Nicole T

    2009-06-01

    Full Text Available Abstract Background The Enteropathogen Resource Integration Center (ERIC; http://www.ericbrc.org has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP, and in particular Information Extraction (IE technology, can be a significant aid to this process. Description We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc. and over 70% for relations (gene/gene product to role, etc. This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application. Conclusion Our Text Mining application is available online on the ERIC website http://www.ericbrc.org/portal/eric/articles. The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations

  1. Review of completed SIMRAC projects at CSIR division of mining technology 1993-1995.

    CSIR Research Space (South Africa)

    Gay, NC

    1997-06-01

    Full Text Available This report was commissioned to assess the improvement of the wellbeing of workers in the mining industry and also verify, if the research findings are successfully implemented in the mines...

  2. You and Technology, A High School Case Study Text.

    Science.gov (United States)

    Damaskos, Nickander J., Ed.; Smyth, Michael P., Ed.

    This second draft of a manuscript for a high school engineering and technology course uses case studies as its format. The principles associated with various engineering problems are presented along with their effects on daily life. Topics include the computer, the automotive power system, satellite communications, the petroleum industry, water…

  3. Access to information technology and willingness to receive text ...

    African Journals Online (AJOL)

    ARTICLE. S. A. J ournal of Child He alth. S. A. J ournal of Child He alth. Background. Effective communication is imperative for the delivery and receipt of adequate health care services. Over the past decade, new technologies and methods of communication have been introduced in Nigeria, especially the Global System ...

  4. Application challenges for information and automation technologies in an underground mine in Brazil

    Directory of Open Access Journals (Sweden)

    Guilherme Paiva

    Full Text Available Abstract Organizations can establish a value-added sustainable strategy by increasing asset utilization. WLAN infrastructure in underground mines tends to be a consistent platform for reliability in data/information management, and assertive governance practices. It enables remote monitoring, registering and data transfer from a control room, avoiding unnecessary human exposure in risk zones while adding agility to operation and maintenance related processes. A series of challenges must be considered so that onboard electronic systems combined with information technology infrastructure can become part of the operational strategy. Leaders must effectively manage to adapt organizations to new standards. This study aims to discuss the challenges related to implement information and automation technologies in an underground mine in Brazil. Data collected in a base metals underground mine is the basis for utilization, reliability and productivity analysis of computerized drilling equipment operating under severe conditions. The study concludes that searching for state-of-the-art operational practices demands a cultural change in organizations, followed by a redefinition of peoples' roles and processes, so technology can provide full integration and be used as a management platform for decision-making.

  5. E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

    OpenAIRE

    Lazard, Allison J.; Saffer, Adam J; Wilcox, Gary B; Chung, Arnold DongWoo; Mackert, Michael S; Bernhardt, Jay M.

    2016-01-01

    Background As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptiv...

  6. LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.

    Science.gov (United States)

    Cañada, Andres; Capella-Gutierrez, Salvador; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso; Krallinger, Martin

    2017-07-03

    A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes-CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  7. Validation of an Improved Computer-Assisted Technique for Mining Free-Text Electronic Medical Records.

    Science.gov (United States)

    Duz, Marco; Marshall, John F; Parkin, Tim

    2017-06-29

    The use of electronic medical records (EMRs) offers opportunity for clinical epidemiological research. With large EMR databases, automated analysis processes are necessary but require thorough validation before they can be routinely used. The aim of this study was to validate a computer-assisted technique using commercially available content analysis software (SimStat-WordStat v.6 (SS/WS), Provalis Research) for mining free-text EMRs. The dataset used for the validation process included life-long EMRs from 335 patients (17,563 rows of data), selected at random from a larger dataset (141,543 patients, ~2.6 million rows of data) and obtained from 10 equine veterinary practices in the United Kingdom. The ability of the computer-assisted technique to detect rows of data (cases) of colic, renal failure, right dorsal colitis, and non-steroidal anti-inflammatory drug (NSAID) use in the population was compared with manual classification. The first step of the computer-assisted analysis process was the definition of inclusion dictionaries to identify cases, including terms identifying a condition of interest. Words in inclusion dictionaries were selected from the list of all words in the dataset obtained in SS/WS. The second step consisted of defining an exclusion dictionary, including combinations of words to remove cases erroneously classified by the inclusion dictionary alone. The third step was the definition of a reinclusion dictionary to reinclude cases that had been erroneously classified by the exclusion dictionary. Finally, cases obtained by the exclusion dictionary were removed from cases obtained by the inclusion dictionary, and cases from the reinclusion dictionary were subsequently reincluded using Rv3.0.2 (R Foundation for Statistical Computing, Vienna, Austria). Manual analysis was performed as a separate process by a single experienced clinician reading through the dataset once and classifying each row of data based on the interpretation of the free-text

  8. PepBank - a database of peptides based on sequence text mining and public peptide data sources

    Directory of Open Access Journals (Sweden)

    Pivovarov Misha

    2007-08-01

    Full Text Available Abstract Background Peptides are important molecules with diverse biological functions and biomedical uses. To date, there does not exist a single, searchable archive for peptide sequences or associated biological data. Rather, peptide sequences still have to be mined from abstracts and full-length articles, and/or obtained from the fragmented public sources. Description We have constructed a new database (PepBank, which at the time of writing contains a total of 19,792 individual peptide entries. The database has a web-based user interface with a simple, Google-like search function, advanced text search, and BLAST and Smith-Waterman search capabilities. The major source of peptide sequence data comes from text mining of MEDLINE abstracts. Another component of the database is the peptide sequence data from public sources (ASPD and UniProt. An additional, smaller part of the database is manually curated from sets of full text articles and text mining results. We show the utility of the database in different examples of affinity ligand discovery. Conclusion We have created and maintain a database of peptide sequences. The database has biological and medical applications, for example, to predict the binding partners of biologically interesting peptides, to develop peptide based therapeutic or diagnostic agents, or to predict molecular targets or binding specificities of peptides resulting from phage display selection. The database is freely available on http://pepbank.mgh.harvard.edu/, and the text mining source code (Peptide::Pubmed is freely available above as well as on CPAN (http://www.cpan.org/.

  9. Vocational retraining of IT-specialists in innovative development of technological process of underground mining of diamonds

    Directory of Open Access Journals (Sweden)

    Алла Викторовна Якурнова

    2012-12-01

    Full Text Available In article features of development of fields in unique climatic conditions of the North of Russia are considered; technological chain of process of production of diamonds; system of preparation of IT specialists in the mining industry; specifics of postgraduate retraining of engineers.

  10. [Exploring the association rules of clinical application of shenmai injection through text mining].

    Science.gov (United States)

    Zhang, Lin-Lin; Guo, Hong-Tao; Zheng, Guang; Liu, Li-Mei; Song, Zhi-Qian; Lu, Ai-Ping; Liu, Zhen-Li

    2013-07-01

    To explore the rules of clinical application of Shenmai Injection (SI). The data sets of SI were downloaded from CBM database by the method of literature retrieved from Jan. 1980 to May 2012. Rules of Chinese medical patterns, diseases, symptoms, Chinese patent medicines (CPM), and Western medicine (WM) were mined out by data slicing algorithm, and they were demonstrated in frequency tables and two-dimension based network. Totally 3 159 literature were recruited. Results showed that SI was most frequently correlated with stasis syndrome and deficiency syndrome. Heart failure, arrhythmia, myocarditis, myocardial infarction, and shock were core diseases treated by SI. Symptoms such as angina pectoris, fatigue, chest tightness/pain were mainly relieved by SI. For CPM, SI was most commonly used with Compound Danshen Injection, Astragalus Injection, and so on. As for WM, SI was most commonly used with nitroglycerin, fructose, captopril, and so on. The syndrome types and mining results of SI were the same with its instructions. Stasis syndrome was the potential Chinese medical pattern of SI. Heart failure, arrhythmia, and myocardial infarction were potential diseases treated by SI. For CPM, SI was most commonly used with Danshen Injection, Compound Danshen Injection, and so on. And for WM, SI was most commonly used with nitroglycerin, fructose, captopril, and so on.

  11. Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

    Science.gov (United States)

    He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

    2017-03-01

    Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

  12. Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD).

    Science.gov (United States)

    Wiegers, Thomas C; Davis, Allan Peter; Cohen, K Bretonnel; Hirschman, Lynette; Mattingly, Carolyn J

    2009-10-08

    The Comparative Toxicogenomics Database (CTD) is a publicly available resource that promotes understanding about the etiology of environmental diseases. It provides manually curated chemical-gene/protein interactions and chemical- and gene-disease relationships from the peer-reviewed, published literature. The goals of the research reported here were to establish a baseline analysis of current CTD curation, develop a text-mining prototype from readily available open source components, and evaluate its potential value in augmenting curation efficiency and increasing data coverage. Prototype text-mining applications were developed and evaluated using a CTD data set consisting of manually curated molecular interactions and relationships from 1,600 documents. Preliminary results indicated that the prototype found 80% of the gene, chemical, and disease terms appearing in curated interactions. These terms were used to re-rank documents for curation, resulting in increases in mean average precision (63% for the baseline vs. 73% for a rule-based re-ranking), and in the correlation coefficient of rank vs. number of curatable interactions per document (baseline 0.14 vs. 0.38 for the rule-based re-ranking). This text-mining project is unique in its integration of existing tools into a single workflow with direct application to CTD. We performed a baseline assessment of the inter-curator consistency and coverage in CTD, which allowed us to measure the potential of these integrated tools to improve prioritization of journal articles for manual curation. Our study presents a feasible and cost-effective approach for developing a text mining solution to enhance manual curation throughput and efficiency.

  13. Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database.

    Science.gov (United States)

    Davis, Allan Peter; Wiegers, Thomas C; Johnson, Robin J; Lay, Jean M; Lennon-Hopkins, Kelley; Saraceni-Richards, Cynthia; Sciaky, Daniela; Murphy, Cynthia Grondin; Mattingly, Carolyn J

    2013-01-01

    The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency.

  14. Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

    Science.gov (United States)

    Johnson, Robin J.; Lay, Jean M.; Lennon-Hopkins, Kelley; Saraceni-Richards, Cynthia; Sciaky, Daniela; Murphy, Cynthia Grondin; Mattingly, Carolyn J.

    2013-01-01

    The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency. PMID:23613709

  15. Studies on medicinal herbs for cognitive enhancement based on the text mining of Dongeuibogam and preliminary evaluation of its effects.

    Science.gov (United States)

    Pak, Malk Eun; Kim, Yu Ri; Kim, Ha Neui; Ahn, Sung Min; Shin, Hwa Kyoung; Baek, Jin Ung; Choi, Byung Tae

    2016-02-17

    In literature on Korean medicine, Dongeuibogam (Treasured Mirror of Eastern Medicine), published in 1613, represents the overall results of the traditional medicines of North-East Asia based on prior medicinal literature of this region. We utilized this medicinal literature by text mining to establish a list of candidate herbs for cognitive enhancement in the elderly and then performed an evaluation of their effects. Text mining was performed for selection of candidate herbs. Cell viability was determined in HT22 hippocampal cells and immunohistochemistry and behavioral analysis was performed in a kainic acid (KA) mice model in order to observe alterations of hippocampal cells and cognition. Twenty four herbs for cognitive enhancement in the elderly were selected by text mining of Dongeuibogam. In HT22 cells, pretreatment with 3 candidate herbs resulted in significantly reduced glutamate-induced cell death. Panax ginseng was the most neuroprotective herb against glutamate-induced cell death. In the hippocampus of a KA mice model, pretreatment with 11 candidate herbs resulted in suppression of caspase-3 expression. Treatment with 7 candidate herbs resulted in significantly enhanced expression levels of phosphorylated cAMP response element binding protein. Number of proliferated cells indicated by BrdU labeling was increased by treatment with 10 candidate herbs. Schisandra chinensis was the most effective herb against cell death and proliferation of progenitor cells and Rehmannia glutinosa in neuroprotection in the hippocampus of a KA mice model. In a KA mice model, we confirmed improved spatial and short memory by treatment with the 3 most effective candidate herbs and these recovered functions were involved in a higher number of newly formed neurons from progenitor cells in the hippocampus. These established herbs and their combinations identified by text-mining technique and evaluation for effectiveness may have value in further experimental and clinical

  16. Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line

    Directory of Open Access Journals (Sweden)

    DeSesa Catherine

    2006-08-01

    Full Text Available Abstract Background Sphingosine 1-phosphate (S1P, a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated. Results To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF, an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1, vascular endothelial growth factor (VEGF, and the urokinase-type plasminogen activator (uPA. This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas. Conclusion Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the

  17. Perfecting technology and organization of development work in coal mines

    Energy Technology Data Exchange (ETDEWEB)

    Fisun, A.P.; Suslo, A.I.

    1980-02-01

    Mining-geological conditions are characterized in the Makeevugol mines, with mining depth over 800 m, methane hazard and rock burst hazard. In the Makeevugol' mines pillar mining predominates as it requires far less development work than a longwall system (detailed comparison is given). Methods of development work, i.e. blasting and heading machines, are described along with haulage systems (belt conveyors, rope haulage ways), and support systems. The article concentrates on development work under difficult conditions, slope up to 26 degrees and methane and rock burst hazard, where possibilities of mechanizing work are more limited than in horizontal and safe coal seams. Work efficiency per one miner in seams characterized by rock burst hazard is 26% lower than in other seams. In the Makeevugol' mines development work presents a serious hindrance to the mining process. (In Russian)

  18. New Forces at Work in Mining: Industry View of Critical Technologies

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, D. J. [Science and Technology Policy Inst., Arlington, VA (United States); LaTourrette, Tom [Science and Technology Policy Inst., Arlington, VA (United States); Bartis, James T. [Science and Technology Policy Inst., Arlington, VA (United States)

    2007-04-01

    RAND has just published a report entitled, "New Forces at Work in Mining: Industry Views of Critical Technologies," by D. J. Peterson, Tom LaTourrette, and James T. Bartis. The report presents the results of a series of in-depth discussions with leading mining industry representatives selected for their prominent position and their ability to think broadly about technology trends. The discussions highlighted the importance of collaborative technology research, development, and implementation strategies and the increasingly critical role of mine personnel in the utilization of new technologies.

  19. Theoretical approaches to creation of robotic coal mines based on the synthesis of simulation technologies

    Science.gov (United States)

    Fryanov, V. N.; Pavlova, L. D.; Temlyantsev, M. V.

    2017-09-01

    Methodological approaches to theoretical substantiation of the structure and parameters of robotic coal mines are outlined. The results of mathematical and numerical modeling revealed the features of manifestation of geomechanical and gas dynamic processes in the conditions of robotic mines. Technological solutions for the design and manufacture of technical means for robotic mine are adopted using the method of economic and mathematical modeling and in accordance with the current regulatory documents. For a comparative performance evaluation of technological schemes of traditional and robotic mines, methods of cognitive modeling and matrix search for subsystem elements in the synthesis of a complex geotechnological system are applied. It is substantiated that the process of technical re-equipment of a traditional mine with a phased transition to a robotic mine will reduce unit costs by almost 1.5 times with a significant social effect due to a reduction in the number of personnel engaged in hazardous work.

  20. ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

    Science.gov (United States)

    2012-01-01

    Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols. PMID:22595088

  1. Text mining, a race against time? An attempt to quantify possible variations in text corpora of medical publications throughout the years.

    Science.gov (United States)

    Wagner, Mathias; Vicinus, Benjamin; Muthra, Sherieda T; Richards, Tereza A; Linder, Roland; Frick, Vilma Oliveira; Groh, Andreas; Rubie, Claudia; Weichert, Frank

    2016-06-01

    The continuous growth of medical sciences literature indicates the need for automated text analysis. Scientific writing which is neither unitary, transcending social situation nor defined by a timeless idea is subject to constant change as it develops in response to evolving knowledge, aims at different goals, and embodies different assumptions about nature and communication. The objective of this study was to evaluate whether publication dates should be considered when performing text mining. A search of PUBMED for combined references to chemokine identifiers and particular cancer related terms was conducted to detect changes over the past 36 years. Text analyses were performed using freeware available from the World Wide Web. TOEFL Scores of territories hosting institutional affiliations as well as various readability indices were investigated. Further assessment was conducted using Principal Component Analysis. Laboratory examination was performed to evaluate the quality of attempts to extract content from the examined linguistic features. The PUBMED search yielded a total of 14,420 abstracts (3,190,219 words). The range of findings in laboratory experimentation were coherent with the variability of the results described in the analyzed body of literature. Increased concurrence of chemokine identifiers together with cancer related terms was found at the abstract and sentence level, whereas complexity of sentences remained fairly stable. The findings of the present study indicate that concurrent references to chemokines and cancer increased over time whereas text complexity remained stable. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. PubMed-EX: a web browser extension to enhance PubMed search with text mining features.

    Science.gov (United States)

    Tsai, Richard Tzong-Han; Dai, Hong-Jie; Lai, Po-Ting; Huang, Chi-Hsin

    2009-11-15

    PubMed-EX is a browser extension that marks up PubMed search results with additional text-mining information. PubMed-EX's page mark-up, which includes section categorization and gene/disease and relation mark-up, can help researchers to quickly focus on key terms and provide additional information on them. All text processing is performed server-side, freeing up user resources. PubMed-EX is freely available at http://bws.iis.sinica.edu.tw/PubMed-EX and http://iisr.cse.yzu.edu.tw:8000/PubMed-EX/.

  3. [The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

    Science.gov (United States)

    Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

    2017-07-12

    To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

  4. Working in a Text Mine; Is Access about to Go down?

    Science.gov (United States)

    Emery, Jill

    2008-01-01

    The age of networked research and networked data analysis is upon us. "Wired Magazine" proclaims on the cover of their July 2008 issue: "The End of Science. The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data. Welcome to the Petabyte Age." Computing technology is sufficiently complex at this point…

  5. Development and Substantiation of Parameters of Environmentally Friendly Technology for Filling the Vertical Mine Workings with Autoclaved Slag-Concrete

    Science.gov (United States)

    Uglyanitca, Andrey; Solonin, Kirill

    2017-11-01

    The environmentally friendly technology for filling the vertical mine workings with autoclaved slag-concrete, prefabricated on the surface of the mine is presented in the article; the optimal parameters of filling technology are proposed. The developed technology for filling the abandoned vertical mine workings allows ensuring the environmental safety of the territories adjacent to the abandoned mine, utilizing slag dumps and providing the possibility of shaft recovery, if necessary, with minimal labor and material costs.

  6. Application of data mining in science and technology management information system based on WebGIS

    Science.gov (United States)

    Wu, Xiaofang; Xu, Zhiyong; Bao, Shitai; Chen, Feixiang

    2009-10-01

    With the rapid development of science and technology and the quick increase of information, a great deal of data is accumulated in the management department of science and technology. Usually, many knowledge and rules are contained and concealed in the data. Therefore, how to excavate and use the knowledge fully is very important in the management of science and technology. It will help to examine and approve the project of science and technology more scientifically and make the achievement transformed as the realistic productive forces easier. Therefore, the data mine technology will be researched and applied to the science and technology management information system to find and excavate the knowledge in the paper. According to analyzing the disadvantages of traditional science and technology management information system, the database technology, data mining and web geographic information systems (WebGIS) technology will be introduced to develop and construct the science and technology management information system based on WebGIS. The key problems are researched in detail such as data mining and statistical analysis. What's more, the prototype system is developed and validated based on the project data of National Natural Science Foundation Committee. The spatial data mining is done from the axis of time, space and other factors. Then the variety of knowledge and rules will be excavated by using data mining technology, which helps to provide an effective support for decisionmaking.

  7. Aspects of Text Mining From Computational Semiotics to Systemic Functional Hypertexts

    OpenAIRE

    Alexander Mehler

    2001-01-01

    The significance of natural language texts as the prime information structure for the management and dissemination of knowledge in organisations is still increasing. Making relevant documents available depending on varying tasks in different contexts is of primary importance for any efficient task completion. Implementing this demand requires the content based processing of texts, which enables to reconstruct or, if necessary, to explore the relationship of task, context and document. Text mi...

  8. Applying WEPP technologies to western alkaline surface coal mines

    Science.gov (United States)

    J. Q. Wu; S. Dun; H. Rhee; X. Liu; W. J. Elliot; T. Golnar; J. R. Frankenberger; D. C. Flanagan; P. W. Conrad; R. L. McNearny

    2011-01-01

    One aspect of planning surface mining operations, regulated by the National Pollutant Discharge Elimination System (NPDES), is estimating potential environmental impacts during mining operations and the reclamation period that follows. Practical computer simulation tools are effective for evaluating site-specific sediment control and reclamation plans for the NPDES....

  9. Data Mining Technologies for Blood Glucose and Diabetes Management

    NARCIS (Netherlands)

    Bellazzi, Riccardo; Abu-Hanna, Ameen

    2009-01-01

    Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management

  10. Data Mining of Acupoint Characteristics from the Classical Medical Text: DongUiBoGam of Korean Medicine

    Directory of Open Access Journals (Sweden)

    Taehyung Lee

    2014-01-01

    Full Text Available Throughout the history of East Asian medicine, different kinds of acupuncture treatment experiences have been accumulated in classical medical texts. Reexamining knowledge from classical medical texts is expected to provide meaningful information that could be utilized in current medical practices. In this study, we used data mining methods to analyze the association between acupoints and patterns of disorder with the classical medical book DongUiBoGam of Korean medicine. Using the term frequency-inverse document frequency (tf-idf method, we quantified the significance of acupoints to its targeting patterns and, conversely, the significance of patterns to acupoints. Through these processes, we extracted characteristics of each acupoint based on its treating patterns. We also drew practical information for selecting acupoints on certain patterns according to their association. Data analysis on DongUiBoGam’s acupuncture treatment gave us an insight into the main idea of DongUiBoGam. We strongly believe that our approach can provide a novel understanding of unknown characteristics of acupoint and pattern identification from the classical medical text using data mining methods.

  11. Using innovative interactive technologies for forming linguistic competence in global mining education

    Directory of Open Access Journals (Sweden)

    Chistyakova Galina

    2017-01-01

    Full Text Available Globalization of mining education imposes new requirements for mining engineer competence. Nowadays linguistic competence is one of the most demanded. It guarantees technical university graduates the possibility of global employment, on the one hand, and the chance of getting cutting edge education in leading training centers of the world, on the other hand. Distance education is actively developing all over the world and is widely used in technical colleges and universities, as well. Interactive method that involves active engagement of students appears to be of the greatest interest due to introduction of modern information and communication technologies for distance learning. The paper presents step-by-step implementation of several interactive technologies (jigsaw, case study, brainstorming, and role-play that can be used in distance education in the process of teaching subjects in foreign languages with the help of information and communication technologies. In response to the changes in the conditions of educational process, the implementation of the methods has been transformed to a combination of traditional (in-class and distance (online learning.

  12. Automatic Building of an Ontology from a Corpus of Text Documents Using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    J. I. Toledo-Alvarado

    2012-06-01

    Full Text Available In this paper we show a procedure to build automatically an ontology from a corpus of text documents without externalhelp such as dictionaries or thesauri. The method proposed finds relevant concepts in the form of multi-words in thecorpus and non-hierarchical relations between them in an unsupervised manner.

  13. Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification

    Directory of Open Access Journals (Sweden)

    Yin Wang

    2016-01-01

    Full Text Available Background. Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. Results. Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. Conclusions. We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.

  14. Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

    Science.gov (United States)

    Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

    2018-02-23

    Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

  15. Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

    Directory of Open Access Journals (Sweden)

    Zhouhao Wang

    2018-01-01

    Full Text Available In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method.

  16. Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification.

    Science.gov (United States)

    Wang, Yin; Li, Rudong; Zhou, Yuhua; Ling, Zongxin; Guo, Xiaokui; Xie, Lu; Liu, Lei

    2016-01-01

    Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.

  17. Data Mining of Acupoint Characteristics from the Classical Medical Text: DongUiBoGam of Korean Medicine.

    Science.gov (United States)

    Lee, Taehyung; Jung, Won-Mo; Lee, In-Seon; Lee, Ye-Seul; Lee, Hyejung; Park, Hi-Joon; Kim, Namil; Chae, Younbyoung

    2014-01-01

    Throughout the history of East Asian medicine, different kinds of acupuncture treatment experiences have been accumulated in classical medical texts. Reexamining knowledge from classical medical texts is expected to provide meaningful information that could be utilized in current medical practices. In this study, we used data mining methods to analyze the association between acupoints and patterns of disorder with the classical medical book DongUiBoGam of Korean medicine. Using the term frequency-inverse document frequency (tf-idf) method, we quantified the significance of acupoints to its targeting patterns and, conversely, the significance of patterns to acupoints. Through these processes, we extracted characteristics of each acupoint based on its treating patterns. We also drew practical information for selecting acupoints on certain patterns according to their association. Data analysis on DongUiBoGam's acupuncture treatment gave us an insight into the main idea of DongUiBoGam. We strongly believe that our approach can provide a novel understanding of unknown characteristics of acupoint and pattern identification from the classical medical text using data mining methods.

  18. PubRunner: A light-weight framework for updating text mining results [version 2; referees: 1 approved, 2 approved with reservations

    Directory of Open Access Journals (Sweden)

    Kishore R. Anekalla

    2017-10-01

    Full Text Available Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications.

  19. Development and testing of a text-mining approach to analyse patients' comments on their experiences of colorectal cancer care.

    Science.gov (United States)

    Wagland, Richard; Recio-Saucedo, Alejandra; Simon, Michael; Bracher, Michael; Hunt, Katherine; Foster, Claire; Downing, Amy; Glaser, Adam; Corner, Jessica

    2016-08-01

    Quality of cancer care may greatly impact on patients' health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients' experiences of care and develop an explanatory model illustrating impact on HRQoL. Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients' specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. The survey response rate was 63.3% (21 802/34 467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments) and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted on HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that

  20. Mining for constructions in texts using N-gram and network analysis

    DEFF Research Database (Denmark)

    Shibuya, Yoshikata; Jensen, Kim Ebensgaard

    2015-01-01

    N-gram analysis to Lewis Carroll's novel Alice's Adventures in Wonderland and Mark Twain's novelThe Adventures of Huckleberry Finn and extrapolate a number of likely constructional phenomena from recurring N-gram patterns in the two texts. In addition to simple N-gram analysis, the following....... The main premise is that, if constructions are functional units, then configurations of words that tend to recur together in discourse are likely to have some sort of function that speakers utilize in discourse. Writers of fiction, for instance, may use constructions in characterizations, mind-styles, text...

  1. Ask and Ye Shall Receive? Automated Text Mining of Michigan Capital Facility Finance Bond Election Proposals to Identify Which Topics Are Associated with Bond Passage and Voter Turnout

    Science.gov (United States)

    Bowers, Alex J.; Chen, Jingjing

    2015-01-01

    The purpose of this study is to bring together recent innovations in the research literature around school district capital facility finance, municipal bond elections, statistical models of conditional time-varying outcomes, and data mining algorithms for automated text mining of election ballot proposals to examine the factors that influence the…

  2. (Text) Mining the LANDscape: Themes and Trends over 40 years of Landscape and Urban Planning

    Science.gov (United States)

    Paul H. Gobster

    2014-01-01

    In commemoration of the journal's 40th anniversary, the co-editor explores themes and trends covered by Landscape and Urban Planning and its parent journals through a qualitative comparison of co-occurrence term maps generated from the text corpora of its abstracts across the four decadal periods of publication.Cluster maps generated from the...

  3. Text mining to detect indications of fraud in annual reports worldwide

    NARCIS (Netherlands)

    Fissette, Marcia Valentine Maria

    2017-01-01

    The research described in this thesis examined the contribution of text analysis to detecting indications of fraud in the annual reports of companies worldwide. A total of 1,727 annual reports have been collected, of which 402 are of the years and companies in which fraudulent activities took place,

  4. Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized versus Common Languages

    Science.gov (United States)

    Jarman, Jay

    2011-01-01

    This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

  5. Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer.

    Science.gov (United States)

    Baker, Simon; Ali, Imran; Silins, Ilona; Pyysalo, Sampo; Guo, Yufan; Högberg, Johan; Stenius, Ulla; Korhonen, Anna

    2017-12-15

    To understand the molecular mechanisms involved in cancer development, significant efforts are being invested in cancer research. This has resulted in millions of scientific articles. An efficient and thorough review of the existing literature is crucially important to drive new research. This time-demanding task can be supported by emerging computational approaches based on text mining which offer a great opportunity to organize and retrieve the desired information efficiently from sizable databases. One way to organize existing knowledge on cancer is to utilize the widely accepted framework of the Hallmarks of Cancer. These hallmarks refer to the alterations in cell behaviour that characterize the cancer cell. We created an extensive Hallmarks of Cancer taxonomy and developed automatic text mining methodology and a tool (CHAT) capable of retrieving and organizing millions of cancer-related references from PubMed into the taxonomy. The efficiency and accuracy of the tool was evaluated intrinsically as well as extrinsically by case studies. The correlations identified by the tool show that it offers a great potential to organize and correctly classify cancer-related literature. Furthermore, the tool can be useful, for example, in identifying hallmarks associated with extrinsic factors, biomarkers and therapeutics targets. CHAT can be accessed at: http://chat.lionproject.net. The corpus of hallmark-annotated PubMed abstracts and the software are available at: http://chat.lionproject.net/about. simon.baker@cl.cam.ac.uk. Supplementary data are available at Bioinformatics online.

  6. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    Science.gov (United States)

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  7. Life priorities in the HIV-positive Asians: a text-mining analysis in young vs. old generation.

    Science.gov (United States)

    Chen, Wei-Ti; Barbour, Russell

    2017-04-01

    HIV/AIDS is one of the most urgent and challenging public health issues, especially since it is now considered a chronic disease. In this project, we used text mining techniques to extract meaningful words and word patterns from 45 transcribed in-depth interviews of people living with HIV/AIDS (PLWHA) conducted in Taipei, Beijing, Shanghai, and San Francisco from 2006 to 2013. Text mining analysis can predict whether an emerging field will become a long-lasting source of academic interest or whether it is simply a passing source of interest that will soon disappear. The data were analyzed by age group (45 and older vs. 44 and younger). The highest ranking fragments in the order of frequency were: "care", "daughter", "disease", "family", "HIV", "hospital", "husband", "medicines", "money", "people", "son", "tell/disclosure", "thought", "want", and "years". Participants in the 44-year-old and younger group were focused mainly on disease disclosure, their families, and their financial condition. In older PLWHA, social supports were one of the main concerns. In this study, we learned that different age groups perceive the disease differently. Therefore, when designing intervention, researchers should consider to tailor an intervention to a specific population and to help PLWHA achieve a better quality of life. Promoting self-management can be an effective strategy for every encounter with HIV-positive individuals.

  8. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

    Science.gov (United States)

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

  9. Technology Transfer at Edgar Mine: Phase 1; October 2016

    Energy Technology Data Exchange (ETDEWEB)

    Augustine, Chad R. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bauer, Stephen [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Nakagawa, Masami [Colorado School of Mines, Golden, CO (United States); Zhou, Wendy [Colorado School of Mines, Golden, CO (United States)

    2017-09-14

    The objective of this project is to study the flow of fluid through the fractures and to characterize the efficiency of heat extraction (heat transfer) from the test rock mass in the Edgar Mine, managed by Colorado School of Mines in Idaho Springs, CO. The experiment consists of drilling into the wall of the mine and fracturing the rock, characterizing the size and nature of the fracture network, circulating fluid through the network, and measuring the efficiency of heat extraction from the 'reservoir' by monitoring the temperature of the 'produced' fluid with time. This is a multi-year project performed as a collaboration between the National Renewable Energy Laboratory, Colorado School of Mines and Sandia National Laboratories and carried out in phases. This report summarizes Phase 1: Selection and characterization of the location for the experiment, and outlines the steps for Phase 2: Circulation Experiments.

  10. Who wrote the "Letter to the Hebrews"?: data mining for detection of text authorship

    Science.gov (United States)

    Sabordo, Madeleine; Chai, Shong Y.; Berryman, Matthew J.; Abbott, Derek

    2005-02-01

    This paper explores the authorship of the Letter to the Hebrews using a number of different measures of relationship between different texts of the New Testament. The methods used in the study include file zipping and compression techniques, prediction by the partial matching technique and the word recurrence interval technique. The long term motivation is that the techniques employed in this study may find applicability in future generation web search engines, email authorship identification, detection of plagiarism and terrorist email traffic filtration.

  11. The Use of Systemic-Functional Linguistics in Automated Text Mining

    Science.gov (United States)

    2009-03-01

    aspects of language in a social- semiotic perspective. Geelong, Vic., Deakin University Press. Halliday, M. A. K. and J. R. Martin (1993). Writing science...natural language processing work for the past 40 years, with recent developments in rule-based and machine learning (ML)- based text processing. An...approach in different contexts. Given that the SFL categories used are applicable to all varieties of language , a number of IR strategies can be

  12. ChemicalTagger: A tool for semantic text-mining in chemistry

    Directory of Open Access Journals (Sweden)

    Hawizy Lezan

    2011-05-01

    Full Text Available Abstract Background The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP approaches. Results We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names. Conclusions It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.

  13. Newspaper archives + text mining = rich sources of historical geo-spatial data

    Science.gov (United States)

    Yzaguirre, A.; Smit, M.; Warren, R.

    2016-04-01

    Newspaper archives are rich sources of cultural, social, and historical information. These archives, even when digitized, are typically unstructured and organized by date rather than by subject or location, and require substantial manual effort to analyze. The effort of journalists to be accurate and precise means that there is often rich geo-spatial data embedded in the text, alongside text describing events that editors considered to be of sufficient importance to the region or the world to merit column inches. A regional newspaper can add over 100,000 articles to its database each year, and extracting information from this data for even a single country would pose a substantial Big Data challenge. In this paper, we describe a pilot study on the construction of a database of historical flood events (location(s), date, cause, magnitude) to be used in flood assessment projects, for example to calibrate models, estimate frequency, establish high water marks, or plan for future events in contexts ranging from urban planning to climate change adaptation. We then present a vision for extracting and using the rich geospatial data available in unstructured text archives, and suggest future avenues of research.

  14. Sports Data Mining Technology Used in Basketball Outcome Prediction

    OpenAIRE

    Cao, Chenjie

    2012-01-01

    Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model fo...

  15. Louhi 2010: Special issue on Text and Data Mining of Health Documents

    Directory of Open Access Journals (Sweden)

    Dalianis Hercules

    2011-07-01

    Full Text Available Abstract The papers presented in this supplement focus and reflect on computer use in every-day clinical work in hospitals and clinics such as electronic health record systems, pre-processing for computer aided summaries, clinical coding, computer decision systems, as well as related ethical concerns and security. Much of this work concerns itself by necessity with incorporation and development of language processing tools and methods, and as such this supplement aims at providing an arena for reporting on development in a diversity of languages. In the supplement we can read about some of the challenges identified above.

  16. Evolution of bayesian-related research over time: a temporal text mining task

    CSIR Research Space (South Africa)

    de Waal, A

    2006-06-01

    Full Text Available ' ' ' '' )()( )()( )|( )|()( , ' ,)1( , 1 )()( , )()( , , α θα θα )( , jzp wd = Slide 10 © CSIR 2006 www.csir.co.za Non-informative Priors: Dirichlet : The conjugate prior distribution for the parameters of the multinomial distribution [6....0065Maximizing0.005classification 0.0101generated0.0083described0.0069few0.0063elucidation 0.0102relationship0.0084learning0.007minimizing0.0064Gaussian Slide 15 © CSIR 2006 www.csir.co.za Results: 2001-2003 0.0077earlier0...

  17. A Text Mining Approach for Extracting Lessons Learned from Project Documentation: An Illustrative Case Study

    Directory of Open Access Journals (Sweden)

    Benjamin Matthies

    2017-12-01

    Full Text Available Lessons learned are important building blocks for continuous learning in project-based organisations. Nonetheless, the practical reality is that lessons learned are often not consistently reused for organisational learning. Two problems are commonly described in this context: the information overload and the lack of procedures and methods for the assessment and implementation of lessons learned. This paper addresses these problems, and appropriate solutions are combined in a systematic lesson learned process. Latent Dirichlet Allocation is presented to solve the first problem. Regarding the second problem, established risk management methods are adapted. The entire lessons learned process will be demonstrated in a practical case study

  18. ChemicalTagger: A tool for semantic text-mining in chemistry.

    Science.gov (United States)

    Hawizy, Lezan; Jessop, David M; Adams, Nico; Murray-Rust, Peter

    2011-05-16

    The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.

  19. Proceedings of the 20. Calgary mining forum: mineral, metals and new technologies 2011

    Energy Technology Data Exchange (ETDEWEB)

    Jewell, Richard; Fourie, Andy

    2011-07-01

    The 20th Calgary Mining Forum was held in Calgary, Alberta, Canada, from April 12th to April 15th, 2011. This conference, minerals, metals and new technologies, was organized by the Calgary Mineral Exploration Group Society and brought together mining industry experts from Alberta and all over the world to discuss new technologies in the industry. The conference was attended by mining industry leaders and government representatives and provided them with the opportunity to hear about some of the latest technologies and studies in the mining industry during short courses, presentations and the keynote address and to network with their peers and share their expertise. 3 out of the 32 papers presented during this conference have been catalogued separately for inclusion in this database.

  20. The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study on Arabidopsis[C][W

    Science.gov (United States)

    Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J.; Inzé, Dirk; Van de Peer, Yves

    2013-01-01

    Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein–protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies. PMID:23532071

  1. E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

    Science.gov (United States)

    2016-01-01

    Background As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter’s streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. Results While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. Conclusions These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. PMID:27956376

  2. Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.

    Science.gov (United States)

    Kocbek, Simon; Cavedon, Lawrence; Martinez, David; Bain, Christopher; Manus, Chris Mac; Haffari, Gholamreza; Zukerman, Ingrid; Verspoor, Karin

    2016-12-01

    Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the

  3. E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter.

    Science.gov (United States)

    Lazard, Allison J; Saffer, Adam J; Wilcox, Gary B; Chung, Arnold DongWoo; Mackert, Michael S; Bernhardt, Jay M

    2016-12-12

    As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter's streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes.

  4. Understanding social collaboration between actors and technology in an automated and digitised deep mining environment.

    Science.gov (United States)

    Sanda, M-A; Johansson, J; Johansson, B; Abrahamsson, L

    2011-10-01

    The purpose of this article is to develop knowledge and learning on the best way to automate organisational activities in deep mines that could lead to the creation of harmony between the human, technical and the social system, towards increased productivity. The findings showed that though the introduction of high-level technological tools in the work environment disrupted the social relations developed over time amongst the employees in most situations, the technological tools themselves became substitute social collaborative partners to the employees. It is concluded that, in developing a digitised mining production system, knowledge of the social collaboration between the humans (miners) and the technology they use for their work must be developed. By implication, knowledge of the human's subject-oriented and object-oriented activities should be considered as an important integral resource for developing a better technological, organisational and human interactive subsystem when designing the intelligent automation and digitisation systems for deep mines. STATEMENT OF RELEVANCE: This study focused on understanding the social collaboration between humans and the technologies they use to work in underground mines. The learning provides an added knowledge in designing technologies and work organisations that could better enhance the human-technology interactive and collaborative system in the automation and digitisation of underground mines.

  5. A text-mining analysis of the public's reactions to the opioid crisis.

    Science.gov (United States)

    Glowacki, Elizabeth M; Glowacki, Joseph B; Wilcox, Gary B

    2017-07-19

    Opioid abuse has become an epidemic in the United States. On August 25, 2016, the former Surgeon General of the United States sent an open letter to care providers asking for their help with combatting this growing health crisis. Social media forums such as Twitter allow for open discussions among the public and up-to-date exchanges of information about timely topics such as opioids. Therefore, the goal of the current study is to identify the public's reactions to the opioid epidemic by identifying the most popular topics tweeted by users. A text miner, algorithmic-driven statistical program was used to capture 73,235 original tweets and retweets posted within a 2-month time span 15 (August 15, 2016, through October 15, 2016). All tweets contained references to "opioids," "turnthetide," or similar keywords. The sets of tweets were then analyzed to identify the most prevalent topics. The most discussed topics had to do with public figures addressing opioid abuse, creating better treatment options for teen addicts, using marijuana as an alternative for managing pain, holding foreign and domestic drug makers accountable for the epidemic, promoting the "Rx for Change" campaign, addressing double standards in the perceptions and treatment of black and white opioid users, and advertising opioid recovery programs. Twitter allows users to find current information, voice their concerns, and share calls for action in response to the opioid epidemic. Monitoring the conversations about opioids that are taking place on social media forums such as Twitter can help public health officials and care providers better understand how the public is responding to this health crisis.

  6. Perfecting technology and organization of development work in coal mines

    Energy Technology Data Exchange (ETDEWEB)

    Rud' , A.M.

    1980-02-01

    Conditions of development work and panelling of coal deposits in Donetskugol' mines are characterized, with mining located 1000 m below the surface and coal seams liable to bursts and methane emission. Air cooling systems used in deep mines during developing and panelling are described. A scheme of a system cooling heading machine oil is given. Water used for cooling is later used for spraying the face to control air dust. Combines, loaders and haulage systems drivage is stressed and organization of work during high speed drivage is discussed. A table shows a comparison of number of miners working in various development headings driven by high speed methods, machines and support systems used, and the economic results of their work, i.e. advance of the heading in one month and labor productivity per miner. (In Russian)

  7. What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques.

    Science.gov (United States)

    Chen, Annie T; Zhu, Shu-Hong; Conway, Mike

    2015-09-29

    The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people's experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people's experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e

  8. Phytostabilization of mine tailings in arid and semiarid environments--an emerging remediation technology.

    Science.gov (United States)

    Mendez, Monica O; Maier, Raina M

    2008-03-01

    Unreclaimed mine tailings sites are a worldwide problem, with thousands of unvegetated, exposed tailings piles presenting a source of contamination for nearby communities. Tailings disposal sites in arid and semiarid environments are especially subject to eolian dispersion and water erosion. Phytostabilization, the use of plants for in situ stabilization of tailings and metal contaminants, is a feasible alternative to costly remediation practices. In this review we emphasize considerations for phytostabilization of mine tailings in arid and semiarid environments, as well as issues impeding its long-term success. We reviewed literature addressing mine closures and revegetation of mine tailings, along with publications evaluating plant ecology, microbial ecology, and soil properties of mine tailings. Data were extracted from peer-reviewed articles and books identified in Web of Science and Agricola databases, and publications available through the U.S. Department of Agriculture, U.S. Environmental Protection Agency, and the United Nations Environment Programme. Harsh climatic conditions in arid and semiarid environments along with the innate properties of mine tailings require specific considerations. Plants suitable for phytostabilization must be native, be drought-, salt-, and metal-tolerant, and should limit shoot metal accumulation. Factors for evaluating metal accumulation and toxicity issues are presented. Also reviewed are aspects of implementing phytostabilization, including plant growth stage, amendments, irrigation, and evaluation. Phytostabilization of mine tailings is a promising remedial technology but requires further research to identify factors affecting its long-term success by expanding knowledge of suitable plant species and mine tailings chemistry in ongoing field trials.

  9. Technological and Profitable Analysis of Airlifting in Deep Sea Mining Systems

    NARCIS (Netherlands)

    Ma, W.; van Rhee, C.; Schott, D.L.

    2017-01-01

    Airlifting technology utilized in deep-sea mining (DSM) industry was proposed in the 70s of last century, which was triggered by the discovery of vast amounts of mineral resources on the seabed. The objective of this paper is to assess the technological feasibility and profitability analyses in

  10. Technological and profitable analysis of airlifting in deep sea mining systems

    NARCIS (Netherlands)

    Ma, W.; van Rhee, C.; Schott, D.L.

    2017-01-01

    Airlifting technology utilized in deep-sea mining (DSM) industry was proposed in the 70s of last century, which was triggered by the discovery of vast amounts of mineral resources on the seabed. The objective of this paper is to assess the technological feasibility and profitability analyses in

  11. Towards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database.

    Science.gov (United States)

    Jamieson, Daniel G; Gerner, Martin; Sarafraz, Farzaneh; Nenadic, Goran; Robertson, David L

    2012-01-01

    Manual curation has long been used for extracting key information found within the primary literature for input into biological databases. The human immunodeficiency virus type 1 (HIV-1), human protein interaction database (HHPID), for example, contains 2589 manually extracted interactions, linked to 14,312 mentions in 3090 articles. The advancement of text-mining (TM) techniques has offered a possibility to rapidly retrieve such data from large volumes of text to a high degree of accuracy. Here, we present a recreation of the HHPID using the current state of the art in TM. To retrieve interactions, we performed gene/protein named entity recognition (NER) and applied two molecular event extraction tools on all abstracts and titles cited in the HHPID. Our best NER scores for precision, recall and F-score were 87.5%, 90.0% and 88.6%, respectively, while event extraction achieved 76.4%, 84.2% and 80.1%, respectively. We demonstrate that over 50% of the HHPID interactions can be recreated from abstracts and titles. Furthermore, from 49 available open-access full-text articles, we extracted a total of 237 unique HIV-1-human interactions, as opposed to 187 interactions recorded in the HHPID from the same articles. On average, we extracted 23 times more mentions of interactions and events from a full-text article than from an abstract and title, with a 6-fold increase in the number of unique interactions. We further demonstrated that more frequently occurring interactions extracted by TM are more likely to be true positives. Overall, the results demonstrate that TM was able to recover a large proportion of interactions, many of which were found within the HHPID, making TM a useful assistant in the manual curation process. Finally, we also retrieved other types of interactions in the context of HIV-1 that are not currently present in the HHPID, thus, expanding the scope of this data set. All data is available at http://gnode1.mib.man.ac.uk/HIV1-text-mining.

  12. Technology for mining overburden with hard inclusions in landslide zones of KATEhK surface mines

    Energy Technology Data Exchange (ETDEWEB)

    Egin, B.A.; Semikobyla, Ya.G. (Leningradskii Gornyi Institut (USSR))

    1990-10-01

    Discusses selected problems associated with overburden removal in the Berezovo and Uryup mines of the Kansk-Achinsk coal basin. The overburden consists of sandstones, clays and aleurites with a moisture content of 15-24%. Hard inclusions (lens) 0.1-4.5 m thick and up to 50 m long consist of sandstones and aleurites. The overburden will be mined by heavy-duty bucket wheel excavators with a capacity from 5,250 to 12,500 m{sup 3}. Effects of moisture content in the overburden and hard lens on excavator operation and landslide hazard are analyzed. Schemes for overburden removal and oversize crushing recommended by 2 research institutes would result in increasing slope angle from 47-50 degrees to 70 degrees and cause a safety factor increase (1.6 times). 2 refs.

  13. Technology applied in the operation and detection of antipersonnel mines: state of the art

    Directory of Open Access Journals (Sweden)

    Javier Andrés Ledezma-Ríos

    2017-06-01

    Full Text Available The main objective of this investigation is to know the different technologies implemented for the detection of antipersonnel mines, documented by different bibliographic means of the latest updates used for the detection of buried objects, the factors that affect the loss of energy of the waves as transmitters of information between them, the characteristics of the soil, the amplitude of the emitted signal, the frequency and the conditions of the terrain. This paper informs about the computational means, of their work with the different algorithms to model correct information of what is happening with the phenomenon of detection. Thus, through this research, the scientific community is informed on the parameters of magnetic susceptibility, the percentage of water and porosity of the environment where the emitted waves react, the difficulty of the stability of the signal to be captured to detect antipersonnel mines, in a geographical context. Currently, PVC tubes, cans, syringes and hand-held devices are being used for their production, and the waves will behave differently against these materials.

  14. Discovery of Teleconnections Using Data Mining Technologies in Global Climate Datasets

    Directory of Open Access Journals (Sweden)

    Fan Lin

    2007-10-01

    Full Text Available In this paper, we apply data mining technologies to a 100-year global land precipitation dataset and a 100-year Sea Surface Temperature (SST dataset. Some interesting teleconnections are discovered, including well-known patterns and unknown patterns (to the best of our knowledge, such as teleconnections between the abnormally low temperature events of the North Atlantic and floods in Northern Bolivia, abnormally low temperatures of the Venezuelan Coast and floods in Northern Algeria and Tunisia, etc. In particular, we use a high dimensional clustering method and a method that mines episode association rules in event sequences. The former is used to cluster the original time series datasets into higher spatial granularity, and the later is used to discover teleconnection patterns among events sequences that are generated by the clustering method. In order to verify our method, we also do experiments on the SOI index and a 100-year global land precipitation dataset and find many well-known teleconnections, such as teleconnections between SOI lower events and drought events of Eastern Australia, South Africa, and North Brazil; SOI lower events and flood events of the middle-lower reaches of Yangtze River; etc. We also do explorative experiments to help domain scientists discover new knowledge.

  15. Decontamination of coal mine effluent generated at the Rajrappa coal mine using phytoremediation technology.

    Science.gov (United States)

    Lakra, Kalpana C; Lal, B; Banerjee, T K

    2017-06-03

    Toxicity of the effluent generated at the Rajrappa coal mine complex under the Central Coalfields Limited (CCL, a subsidiary of Coal India Limited) in Jharkhand, India was investigated. The concentrations (mg L-1) of all the toxic metals (Fe, Mn, Ni, Zn, Cu, Pb, Cr, and Cd) in the coal mine effluent were above the safe limit suggested by the Environmental Protection Agency (EPA 2003). Among these, Fe showed the highest concentration (18.21 ± 3.865), while Cr had the lowest effluent concentration (0.15 ± 0.014). Efforts were also made to detoxify the effluent using two species of aquatic macrophytes namely "'Salvinia molesta and Pistia stratiotes." After 10 days of phytoremediation, S. molesta removed Pb (96.96%) > Ni (97.01%) > Cu (96.77%) > Zn (96.38%) > Mn (96.22%) > Fe (94.12%) > Cr (92.85%) > Cd (80.99%), and P. stratiotes removed Pb (96.21%) > Fe (94.34%) > Ni (92.53%) > Mn (85.24%) > Zn (79.51%) > Cr (78.57%) > Cu (74.19%) > Cd (72.72%). The impact of coal mine exposure on chlorophyll content showed a significant decrease of 42.49% and 24.54% from control values in S. molesta and P. stratiotes, respectively, perhaps due to the damage inflicted by the toxic metals, leading to the decay of plant tissues.

  16. Promising Technologies of Mining and Processing of Solid Minerals

    Directory of Open Access Journals (Sweden)

    Shabaev Sergey

    2017-01-01

    Full Text Available The continuing growth in mineral extraction entails an increase in industrial waste, which in turn has a negative impact on the environment. Rubber-tired vehicles, in which the tires wear colossally, is mainly used as a transport for loading, unloading, transportation and other types of work in the extraction of solid minerals. The used tires are not disposed in any way, but are stored in special areas where harmful toxic substances are emitted under the influence of ultraviolet rays. Therefore, a decision was made to find a method for utilization and rational use of industrial waste in the road construction sector. The operating temperature of composite rubber-bituminous binders based on rubber crumb from the used automobile tires is estimated in this paper, which is necessary for assigning technological parameters of production and laying of asphalt-concrete mixtures produced on their basis. It is established that composite rubber-bituminous binders based on rubber chips from the used automobile tires, produced according to the two-stage technology, have the same viscosity as the original petroleum bitumen, at a temperature increased by 20°C.

  17. A concept for the modernization of underground mining master maps based on the enrichment of data definitions and spatial database technology

    Directory of Open Access Journals (Sweden)

    Krawczyk Artur

    2018-01-01

    Full Text Available In this article, topics regarding the technical and legal aspects of creating digital underground mining maps are described. Currently used technologies and solutions for creating, storing and making digital maps accessible are described in the context of the Polish mining industry. Also, some problems with the use of these technologies are identified and described. One of the identified problems is the need to expand the range of mining map data provided by survey departments to other mining departments, such as ventilation maintenance or geological maintenance. Three solutions are proposed and analyzed, and one is chosen for further analysis. The analysis concerns data storage and making survey data accessible not only from paper documentation, but also directly from computer systems. Based on enrichment data, new processing procedures are proposed for a new way of presenting information that allows the preparation of new cartographic representations (symbols of data with regard to users’ needs.

  18. Perfecting technology and organization of development work in coal mines

    Energy Technology Data Exchange (ETDEWEB)

    Shmigol, A.V.

    1980-02-01

    Haulage systems used in fully mechanized development headings in the western part of the Donbass are evaluated. 97% of loading is mechanized, 95% of mine drivage is done by heading machines. Haulage systems used in horizontal seams are evaluated: electric locomotives and loading conveyors combined with heading machines (shown in a diagram). Haulage systems in inclined seams include: belt conveyors for haulage of rocks and rope haulage way for transporting materials (also depicted). Rocks are loaded on a belt conveyor by a short (30 m) conveyor suspended on a monorail and moving together with the heading machine. Another version of haulage in inclined seams is a rope haulage way used for hauling mined material and materials and a loading conveyor towed by a heading combine. The described haulage systems and their productivity are evaluated. (In Russian)

  19. Analysis of US underground thin seam mining potential. Volume 1. Text. Final technical report, December 1978. [In thin seams

    Energy Technology Data Exchange (ETDEWEB)

    Pimental, R. A; Barell, D.; Fine, R. J.; Douglas, W. J.

    1979-06-01

    An analysis of the potential for US underground thin seam (< 28'') coal mining is undertaken to provide basic information for use in making a decision on further thin seam mining equipment development. The characteristics of the present low seam mines and their mining methods are determined, in order to establish baseline data against which changes in mine characteristics can be monitored as a function of time. A detailed data base of thin seam coal resources is developed through a quantitative and qualitative analysis at the bed, county and state level. By establishing present and future coal demand and relating demand to production and resources, the market for thin seam coal has been identified. No thin seam coal demand of significance is forecast before the year 2000. Current uncertainty as to coal's future does not permit market forecasts beyond the year 2000 with a sufficient level of reliability.

  20. What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques

    Science.gov (United States)

    Zhu, Shu-Hong; Conway, Mike

    2015-01-01

    Background The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people’s experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people’s experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons

  1. Implicit prosody mining based on the human eye image capture technology

    Science.gov (United States)

    Gao, Pei-pei; Liu, Feng

    2013-08-01

    The technology of eye tracker has become the main methods of analyzing the recognition issues in human-computer interaction. Human eye image capture is the key problem of the eye tracking. Based on further research, a new human-computer interaction method introduced to enrich the form of speech synthetic. We propose a method of Implicit Prosody mining based on the human eye image capture technology to extract the parameters from the image of human eyes when reading, control and drive prosody generation in speech synthesis, and establish prosodic model with high simulation accuracy. Duration model is key issues for prosody generation. For the duration model, this paper put forward a new idea for obtaining gaze duration of eyes when reading based on the eye image capture technology, and synchronous controlling this duration and pronunciation duration in speech synthesis. The movement of human eyes during reading is a comprehensive multi-factor interactive process, such as gaze, twitching and backsight. Therefore, how to extract the appropriate information from the image of human eyes need to be considered and the gaze regularity of eyes need to be obtained as references of modeling. Based on the analysis of current three kinds of eye movement control model and the characteristics of the Implicit Prosody reading, relative independence between speech processing system of text and eye movement control system was discussed. It was proved that under the same text familiarity condition, gaze duration of eyes when reading and internal voice pronunciation duration are synchronous. The eye gaze duration model based on the Chinese language level prosodic structure was presented to change previous methods of machine learning and probability forecasting, obtain readers' real internal reading rhythm and to synthesize voice with personalized rhythm. This research will enrich human-computer interactive form, and will be practical significance and application prospect in terms of

  2. Use of text-mining methods to improve efficiency in the calculation of drug exposure to support pharmacoepidemiology studies.

    Science.gov (United States)

    McTaggart, Stuart; Nangle, Clifford; Caldwell, Jacqueline; Alvarez-Madrazo, Samantha; Colhoun, Helen; Bennie, Marion

    2018-02-06

    Efficient generation of structured dose instructions that enable researchers to calculate drug exposure is central to pharmacoepidemiology studies. Our aim was to design and test an algorithm to codify dose instructions, applied to the NHS Scotland Prescribing Information System (PIS) that records about 100 million prescriptions per annum. A natural language processing (NLP) algorithm was developed that enabled free-text dose instructions to be represented by three attributes - quantity, frequency and qualifier - specified by three, three and two variables, respectively. A sample of 15 593 distinct dose instructions was used to test, validate and refine the algorithm. The final algorithm used a zero-assumption approach and was then applied to the full dataset. The initial algorithm generated structured output for 13 152 (84.34%) of the 15 593 sample dose instructions, and reviewers identified 767 (5.83%) incorrect translations, giving an accuracy of 94.17%. Following subsequent refinement of the algorithm rules, application to the full dataset of 458 227 687 prescriptions (99.67% had dose instructions represented by 4 964 083 distinct instructions) generated a structured output for 92.3% of dose instruction texts. This varied by therapeutic area (from 86.7% for the central nervous system to 96.8% for the cardiovascular system). We created an NLP algorithm, operational at scale, to produce structured output that gives data users maximum flexibility to formulate, test and apply their own assumptions according to the medicines under investigation. Text mining approaches can provide a solution to the safe and efficient management and provisioning of large volumes of data generated through our health systems.

  3. Identification of candidate genes in Populus cell wall biosynthesis using text-mining, co-expression network and comparative genomics

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaohan [ORNL; Ye, Chuyu [ORNL; Bisaria, Anjali [ORNL; Tuskan, Gerald A [ORNL; Kalluri, Udaya C [ORNL

    2011-01-01

    Populus is an important bioenergy crop for bioethanol production. A greater understanding of cell wall biosynthesis processes is critical in reducing biomass recalcitrance, a major hindrance in efficient generation of ethanol from lignocellulosic biomass. Here, we report the identification of candidate cell wall biosynthesis genes through the development and application of a novel bioinformatics pipeline. As a first step, via text-mining of PubMed publications, we obtained 121 Arabidopsis genes that had the experimental evidences supporting their involvement in cell wall biosynthesis or remodeling. The 121 genes were then used as bait genes to query an Arabidopsis co-expression database and additional genes were identified as neighbors of the bait genes in the network, increasing the number of genes to 548. The 548 Arabidopsis genes were then used to re-query the Arabidopsis co-expression database and re-construct a network that captured additional network neighbors, expanding to a total of 694 genes. The 694 Arabidopsis genes were computationally divided into 22 clusters. Queries of the Populus genome using the Arabidopsis genes revealed 817 Populus orthologs. Functional analysis of gene ontology and tissue-specific gene expression indicated that these Arabidopsis and Populus genes are high likelihood candidates for functional genomics in relation to cell wall biosynthesis.

  4. Grouping chemicals for health risk assessment: A text mining-based case study of polychlorinated biphenyls (PCBs).

    Science.gov (United States)

    Ali, Imran; Guo, Yufan; Silins, Ilona; Högberg, Johan; Stenius, Ulla; Korhonen, Anna

    2016-01-22

    As many chemicals act as carcinogens, chemical health risk assessment is critically important. A notoriously time consuming process, risk assessment could be greatly supported by classifying chemicals with similar toxicological profiles so that they can be assessed in groups rather than individually. We have previously developed a text mining (TM)-based tool that can automatically identify the mode of action (MOA) of a carcinogen based on the scientific evidence in literature, and it can measure the MOA similarity between chemicals on the basis of their literature profiles (Korhonen et al., 2009, 2012). A new version of the tool (2.0) was recently released and here we apply this tool for the first time to investigate and identify meaningful groups of chemicals for risk assessment. We used published literature on polychlorinated biphenyls (PCBs)-persistent, widely spread toxic organic compounds comprising of 209 different congeners. Although chemically similar, these compounds are heterogeneous in terms of MOA. We show that our TM tool, when applied to 1648 PubMed abstracts, produces a MOA profile for a subgroup of dioxin-like PCBs (DL-PCBs) which differs clearly from that for the rest of PCBs. This suggests that the tool could be used to effectively identify homogenous groups of chemicals and, when integrated in real-life risk assessment, could help and significantly improve the efficiency of the process. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE.

    Science.gov (United States)

    Névéol, Aurélie; Wilbur, W John; Lu, Zhiyong

    2012-01-01

    High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/

  6. Analysis of Protein Phosphorylation and Its Functional Impact on Protein-Protein Interactions via Text Mining of the Scientific Literature.

    Science.gov (United States)

    Wang, Qinghua; Ross, Karen E; Huang, Hongzhan; Ren, Jia; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

    2017-01-01

    Post-translational modifications (PTMs) are one of the main contributors to the diversity of proteoforms in the proteomic landscape. In particular, protein phosphorylation represents an essential regulatory mechanism that plays a role in many biological processes. Protein kinases, the enzymes catalyzing this reaction, are key participants in metabolic and signaling pathways. Their activation or inactivation dictate downstream events: what substrates are modified and their subsequent impact (e.g., activation state, localization, protein-protein interactions (PPIs)). The biomedical literature continues to be the main source of evidence for experimental information about protein phosphorylation. Automatic methods to bring together phosphorylation events and phosphorylation-dependent PPIs can help to summarize the current knowledge and to expose hidden connections. In this chapter, we demonstrate two text mining tools, RLIMS-P and eFIP, for the retrieval and extraction of kinase-substrate-site data and phosphorylation-dependent PPIs from the literature. These tools offer several advantages over a literature search in PubMed as their results are specific for phosphorylation. RLIMS-P and eFIP results can be sorted, organized, and viewed in multiple ways to answer relevant biological questions, and the protein mentions are linked to UniProt identifiers.

  7. Clustering box office movie with Partition Around Medoids (PAM) Algorithm based on Text Mining of Indonesian subtitle

    Science.gov (United States)

    Alfarizy, A. D.; Indahwati; Sartono, B.

    2017-03-01

    Indonesia is the largest Hollywood movie industry target market in Southeast Asia in 2015. Hollywood movies distributed in Indonesia targeted people in all range of ages including children. Low awareness of guiding children while watching movies make them could watch any rated films even the unsuitable ones for their ages. Even after being translated into Bahasa and passed the censorship phase, words that uncomfortable for children to watch still exist. The purpose of this research is to cluster box office Hollywood movies based on Indonesian subtitle, revenue, IMDb user rating and genres as one of the reference for adults to choose right movies for their children to watch. Text mining is used to extract words from the subtitles and count the frequency for three group of words (bad words, sexual words and terror words), while Partition Around Medoids (PAM) Algorithm with Gower similarity coefficient as proximity matrix is used as clustering method. We clustered 624 movies from 2006 until first half of 2016 from IMDb. Cluster with highest silhouette coefficient value (0.36) is the one with 5 clusters. Animation, Adventure and Comedy movies with high revenue like in cluster 5 is recommended for children to watch, while Comedy movies with high revenue like in cluster 4 should be avoided to watch.

  8. Study on Students' Impression Data in Practical Training Using Text Mining Method-Analysis of Considerable Communication.

    Science.gov (United States)

    Teramachi, Hitomi; Sugita, Ikuto; Ino, Yoko; Hayashi, Yuta; Yoshida, Aki; Otsubo, Manami; Ueno, Anri; Katsuno, Hayato; Noguchi, Yoshihiro; Iguchi, Kazuhiro; Tachi, Tomoya

    2017-09-01

    We analyzed impression data and the scale of communication skills of students using text mining method to clarify which area a student was conscious of in communication in practical training. The results revealed that students tended to be conscious of the difference between practical hospital training and practical pharmacy training. In practical hospital training, specific expressions denoting relationships were "patient-visit", "counseling-conduct", "patient-counseling", and "patient-talk". In practical pharmacy training, specific expressions denoting relationships were "patient counseling-conduct", "story-listen", "patient-many", and "patient-visit". In practical hospital training, the word "patient" was connected to many words suggesting that students were conscious of a patient-centered communication. In practical pharmacy training, words such as "patient counseling", "patient", and "explanation" were placed in center and connected with many other words and there was an independent relationship between "communication" and "accept". In conclusion, it was suggested that students attempted active patient-centered communication in practical hospital training, while they were conscious of listening closely in patient counseling in practical pharmacy training.

  9. miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

    Science.gov (United States)

    Gupta, Samir; Ross, Karen E; Tudor, Catalina O; Wu, Cathy H; Schmidt, Carl J; Vijay-Shanker, K

    2016-04-29

    MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation. We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence. miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to

  10. Facilitating Learning from Technology-Enhanced Text: Effects of Prompted Elaborative Interrogation

    Science.gov (United States)

    Dornisch, Michele M.; Sperling, Rayne A.

    2006-01-01

    The authors examined the use of the elaborative interrogation (EI) strategy with a lengthy text in a technology-enhanced environment. As commonly found in traditional and online text materials, questions appeared in the right margins of the text. Seventy-five randomly assigned volunteers in 2 conditions read instructional materials delivered by…

  11. Automated Text Data Mining Analysis of Five Decades of Educational Leadership Research Literature: Probabilistic Topic Modeling of "EAQ" Articles From 1965 to 2014

    Science.gov (United States)

    Wang, Yinying; Bowers, Alex J.; Fikis, David J.

    2017-01-01

    Purpose: The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in…

  12. Technology for Mining the Big Data of MOOCs

    Science.gov (United States)

    O'Reilly, Una-May; Veeramachaneni, Kalyan

    2014-01-01

    Because MOOCs bring big data to the forefront, they confront learning science with technology challenges. We describe an agenda for developing technology that enables MOOC analytics. Such an agenda needs to efficiently address the detailed, low level, high volume nature of MOOC data. It also needs to help exploit the data's capacity to reveal, in…

  13. The Health and Safety Benefits of New Technologies in Mining: A Review and Strategy for Designing and Deploying Effective User-Centred Systems

    Directory of Open Access Journals (Sweden)

    Tim Horberry

    2012-10-01

    Full Text Available Mining is currently experiencing a rapid growth in the development and uptake of automation and other new technologies (such as collision detection systems; however, they are often developed from a technology-centred perspective that does not explicitly consider the end-user. This paper first presents a review of the technologies currently available (or near-market and the likely human factors issues associated with them. The second part of the paper presents a potential long term strategy for research and development that aims to maximise the safety and health benefits for operators of such new technologies. The strategy includes a four stage research and development process, this covers: better understanding the needs for technology, user requirements and risk/cost analysis; human element design, procurement and deployment processes; evaluation and verification of the strategy; and dissemination of it to relevant stakeholders (including equipment manufacturers, mine site purchasers and regulators. The paper concludes by stressing the importance of considering the human element with respect to new mining technologies and the likely benefits of adopting the type of strategy proposed here. The overall vision is for mining to become safer and healthier through effective user-centred design and deployment of new technologies that serve both operator needs and the demands of the workplace.

  14. A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

    Science.gov (United States)

    Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

    2017-08-01

    The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

  15. COMPOST-FREE BIOREACTOR TREATMENT OF ACID ROCK DRAINAGE LEVIATHAN MINE, CALIFORNIA INNOVATIVE TECHNOLOGY EVALUATION REPORT

    Science.gov (United States)

    As part of the Superfund Innovative Technology Evaluation (SITE) program, an evaluation of the compost-free bioreactor treatment of acid rock drainage (ARD) from the Aspen Seep was conducted at the Leviathan Mine Superfund site located in a remote, high altitude area of Alpine Co...

  16. Advances in geophysical technologies for the exploration and safe mining of deep gold ore bodies in the Witwatersrand basin, South Africa

    CSIR Research Space (South Africa)

    Durrheim, RJ

    2013-08-01

    Full Text Available -1 Proceedings of the Twelfth Biennial Meeting of the Society for Geology Applied to Mineral Deposits, Uppsala, Sweden, 12-15 August 2013 Advances in geophysical technologies for the exploration and safe mining of deep gold ore bodies in the Witwatersrand...

  17. Phytostabilization of Mine Tailings in Arid and Semiarid Environments—An Emerging Remediation Technology

    Science.gov (United States)

    Mendez, Monica O.; Maier, Raina M.

    2008-01-01

    Objective Unreclaimed mine tailings sites are a worldwide problem, with thousands of unvegetated, exposed tailings piles presenting a source of contamination for nearby communities. Tailings disposal sites in arid and semiarid environments are especially subject to eolian dispersion and water erosion. Phytostabilization, the use of plants for in situ stabilization of tailings and metal contaminants, is a feasible alternative to costly remediation practices. In this review we emphasize considerations for phytostabilization of mine tailings in arid and semiarid environments, as well as issues impeding its long-term success. Data sources We reviewed literature addressing mine closures and revegetation of mine tailings, along with publications evaluating plant ecology, microbial ecology, and soil properties of mine tailings. Data extraction Data were extracted from peer-reviewed articles and books identified in Web of Science and Agricola databases, and publications available through the U.S. Department of Agriculture, U.S. Environmental Protection Agency, and the United Nations Environment Programme. Data synthesis Harsh climatic conditions in arid and semiarid environments along with the innate properties of mine tailings require specific considerations. Plants suitable for phytostabilization must be native, be drought-, salt-, and metal-tolerant, and should limit shoot metal accumulation. Factors for evaluating metal accumulation and toxicity issues are presented. Also reviewed are aspects of implementing phytostabilization, including plant growth stage, amendments, irrigation, and evaluation. Conclusions Phytostabilization of mine tailings is a promising remedial technology but requires further research to identify factors affecting its long-term success by expanding knowledge of suitable plant species and mine tailings chemistry in ongoing field trials. PMID:18335091

  18. The Mineral Question: How Energy and Technology will determine the Future of Mining

    Directory of Open Access Journals (Sweden)

    Ugo eBardi

    2013-12-01

    Full Text Available Almost 150 years after that William Stanley Jevons published his paper The Coal Question (Jevons, 1866 the debate on mineral depletion has been ongoing between two main schools of thought: one that sees depletion as an important problem for the near future and another that sees technology and human ingenuity as the most important factors in making depletion a problem for the remote future. Today, however, we have created intellectual tools that permit us to frame the problem on the basis of physical factors, in particular on the basis of thermodynamics. The present paper examines the problem of mineral depletion from a broad viewpoint, with a specific view on the role of energy in the mining and production processes. The conclusion is that energy is a fundamental factor in determining how long we can expect the supply of mineral resources to last at the present prices and production levels. The rapid depletion of our main energy resources, fossil fuels, is creating a serious supply problem that is already being felt in terms of high prices of all mineral commodities. Technology can mitigate the problem, but not solve it. In a non remote future, the world's industrial system will have to undergo fundamental changes in order to adapt to a reduced supply of mineral commodities.

  19. Data mining in radiology.

    Science.gov (United States)

    Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

    2014-04-01

    Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining.

  20. Drivers of change in the development of technology and management of mining machinery

    Energy Technology Data Exchange (ETDEWEB)

    Bhattacharya, J.; Gupta, S. [Indian Institute of Technology, Kharagpur (India). Dept. of Mining Engineering

    1999-05-01

    Mining machinery has been improved to enable fast and easy repair, with in-built condition monitoring. The article discusses desirable criteria for today's mining machinery, such as reduced power consumption, operational flexibility, reduced time losses, operator comfort and multiple operational mode. Information technology has enabled operators to send in volumes of information that were unthinkable a decade ago. The article goes on to describe new developments in LDH, underground trucks, remote control navigation, underground shearers with automatic controls, cutting tools, diesel engines and lubrication and hydraulic systems. Software is now available for maintenance management. 10 refs., 5 figs.

  1. The modernisation of mining

    CSIR Research Space (South Africa)

    Ritchken, E

    2017-10-01

    Full Text Available This presentation discusses the modernisation of mining. The presentation focuses on the mining clusters, Mining Challenges, Compliance versus Collaboration, The Phakisa, The Mining Precinct & the Mining Hub also Win-Win Beneficiation: Iron...

  2. Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis.

    Science.gov (United States)

    Vyas, Renu; Bapat, Sanket; Jain, Esha; Karthikeyan, Muthukumarasamy; Tambe, Sanjeev; Kulkarni, Bhaskar D

    2016-12-01

    In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein-protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Text mining of rheumatoid arthritis and diabetes mellitus to understand the mechanisms of Chinese medicine in different diseases with same treatment.

    Science.gov (United States)

    Zhao, Ning; Zheng, Guang; Li, Jian; Zhao, Hong-Yan; Lu, Cheng; Jiang, Miao; Zhang, Chi; Guo, Hong-Tao; Lu, Ai-Ping

    2018-01-09

    To identify the commonalities between rheumatoid arthritis (RA) and diabetes mellitus (DM) to understand the mechanisms of Chinese medicine (CM) in different diseases with the same treatment. A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verifified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identifified via text mining was used as the intervention. Similarities were identifified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflfl ammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflfl ammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM. Commonalities between the biological activities involved in RA and DM were identifified through text mining, and both RA and DM might be responsive to the same intervention at a specifific stage.

  4. PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords.

    Science.gov (United States)

    Chen, Chou-Cheng; Ho, Chung-Liang

    2014-01-01

    While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research. http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx.

  5. Using Cluster Analysis for Data Mining in Educational Technology Research

    Science.gov (United States)

    Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

    2012-01-01

    Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

  6. Promising Technologies of Mining and Processing of Solid Minerals

    Science.gov (United States)

    Shabaev, Sergey; Ivanov, Seregey; Vakhianov, Evgeniy

    2017-11-01

    The continuing growth in mineral extraction entails an increase in industrial waste, which in turn has a negative impact on the environment. Rubber-tired vehicles, in which the tires wear colossally, is mainly used as a transport for loading, unloading, transportation and other types of work in the extraction of solid minerals. The used tires are not disposed in any way, but are stored in special areas where harmful toxic substances are emitted under the influence of ultraviolet rays. Therefore, a decision was made to find a method for utilization and rational use of industrial waste in the road construction sector. The operating temperature of composite rubber-bituminous binders based on rubber crumb from the used automobile tires is estimated in this paper, which is necessary for assigning technological parameters of production and laying of asphalt-concrete mixtures produced on their basis. It is established that composite rubber-bituminous binders based on rubber chips from the used automobile tires, produced according to the two-stage technology, have the same viscosity as the original petroleum bitumen, at a temperature increased by 20°C.

  7. Longwall mining

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-03-14

    As part of EIA`s program to provide information on coal, this report, Longwall-Mining, describes longwall mining and compares it with other underground mining methods. Using data from EIA and private sector surveys, the report describes major changes in the geologic, technological, and operating characteristics of longwall mining over the past decade. Most important, the report shows how these changes led to dramatic improvements in longwall mining productivity. For readers interested in the history of longwall mining and greater detail on recent developments affecting longwall mining, the report includes a bibliography.

  8. Application of Text Mining to Extract Hotel Attributes and Construct Perceptual Map of Five Star Hotels from Online Review: Study of Jakarta and Singapore Five-Star Hotels

    Directory of Open Access Journals (Sweden)

    Arga Hananto

    2015-12-01

    Full Text Available The use of post-purchase online consumer review in hotel attributes study was still scarce in the literature. Arguably, post purchase online review data would gain more accurate attributes thatconsumers actually consider in their purchase decision. This study aims to extract attributes from two samples of five-star hotel reviews (Jakarta and Singapore with text mining methodology. In addition,this study also aims to describe positioning of five-star hotels in Jakarta and Singapore based on the extracted attributes using Correspondence Analysis. This study finds that reviewers of five star hotels in both cities mentioned similar attributes such as service, staff, club, location, pool and food. Attributes derived from text mining seem to be viable input to build fairly accurate positioning map of hotels. This study has demonstrated the viability of online review as a source of data for hotel attribute and positioning studies.

  9. [Exploring application of data mining technology in researching composition rules of Tibetan medical formulas].

    Science.gov (United States)

    Cairang, Nanjia; Renzeng, Duojie; Duojie, Cairang; Luosang, Dongzhi; Li, Xianjia

    2012-08-01

    There are thousands of medicinal formulas in the ancient Tibetan medical texts. Researching the composition rules of Tibetan medical formulas is a very important step in the study and practice of Tibetan medicine. In order to explore the composition rules of Tibetan medical formulas this article draws on the research methods utilized in related fields of traditional Chinese medicine adapted to the unique characteristics of Tibetan medicine. This is the first time the utilization of data mining methods has been proposed for the research of Tibetan medical formulas. It is believed that data mining techniques can aid researchers in discovering the composition rules of Tibetan medical formulas in accordance with Tibetan medical theory.

  10. Technology of mine stone packing in abandoned workings

    Energy Technology Data Exchange (ETDEWEB)

    Neshitin, V.M.; Kurchenko, Eh.P.; Afanas' ev, V.V. (DonUGI (USSR))

    1990-03-01

    Describes technology of stone packing into abandoned workings using a scraper and a slinging machine of belt drum type. Three schemes are presented, one that uses the BS-4P scraper, the 1LP winch and a slinging machine, a second one that uses the UZM slinger with the stone delivered by a conveyor and a third scheme that uses the UZM slinger, 1PPN-5 loader and the OB or BOM tippler. The first and third schemes can be used in two-way workings with cross-section area not less than 9 m{sup 2} and the second scheme in workings with cross-section area not under 6 m{sup 2}. The rate of packing is 168, 236 and 248 m{sup 3}/d respectively. The cost of packing operations is 2.36, 2.82 and 2.46 rubles/t respectively which is 0.99-1.45 less than in the case of hauling and hoisting waste rock to the surface. Organization of packing operations is described.

  11. Space Resource Utilization: Technologies and Potential Synergism with Terrestrial Mining

    Science.gov (United States)

    Sanders, Gerald B.

    2015-01-01

    Space Resources and Their Uses: The idea of using resources in space to support human exploration and settlement or for economic development and profit beyond the surface of Earth has been proposed and discussed for decades. Work on developing a method to extract oxygen from lunar regolith started even before humans set foot on the Moon for the first time. The use of space resources, commonly referred to as In Situ Resource Utilization (ISRU), involves the processes and operations to harness and utilize resources in space (both natural and discarded) to create products for subsequent use. Potential space resources include water, solar wind implanted volatiles (hydrogen, helium, carbon, nitrogen, etc.), vast quantities of metals and minerals in extraterrestrial soils, atmospheric constituents, unlimited solar energy, regions of permanent light and darkness, the vacuum and zero-gravity of space itself, trash and waste from human crew activities, and discarded hardware that has completed its primary purpose. ISRU covers a wide variety of concepts, technical disciplines, technologies, and processes. When considering all aspects of ISRU, there are 5 main areas that are relevant to human space exploration and the commercialization of space: 1. Resource Characterization and Mapping, 2. In Situ Consumables Production, 3. Civil Engineering and Construction, 4. In Situ Energy Production and Storage, and 5. In Situ Manufacturing.

  12. Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

    Science.gov (United States)

    Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

    2000-01-01

    These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

  13. Exploratory analysis of textual data from the Mother and Child Handbook using the text-mining method: Relationships with maternal traits and post-partum depression.

    Science.gov (United States)

    Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Sato, Shuhei; Ohwada, Michitaka

    2016-06-01

    The aim of the present study was to examine the possibility of screening apprehensive pregnant women and mothers at risk for post-partum depression from an analysis of the textual data in the Mother and Child Handbook by using the text-mining method. Uncomplicated pregnant women (n = 58) were divided into two groups according to State-Trait Anxiety Inventory grade (high trait [group I, n = 21] and low trait [group II, n = 37]) or Edinburgh Postnatal Depression Scale score (high score [group III, n = 15] and low score [group IV, n = 43]). An exploratory analysis of the textual data from the Maternal and Child Handbook was conducted using the text-mining method with the Word Miner software program. A comparison of the 'structure elements' was made between the two groups. The number of structure elements extracted by separated words from text data was 20 004 and the number of structure elements with a threshold of 2 or more as an initial value was 1168. Fifteen key words related to maternal anxiety, and six key words related to post-partum depression were extracted. The text-mining method is useful for the exploratory analysis of textual data obtained from pregnant woman, and this screening method has been suggested to be useful for apprehensive pregnant women and mothers at risk for post-partum depression. © 2016 Japan Society of Obstetrics and Gynecology.

  14. Text-Mining and Gamification for the Qualification of Service Technicians in the Maintenance Industry of Offshore Wind Energy

    Directory of Open Access Journals (Sweden)

    Thies Beinke

    2017-04-01

    Full Text Available The competition of maintenance services in the offshore wind industry is continually increasing. The quality of the services acts as the distinguishing feature in the industry. Furthermore, there are public standards, which lead to the permanent necessity to offer further education and training programs for employees. To meet the requirements for further training in the specific field of application within the offshore wind industry, a gamified e-learning application has been developed and is introduced in this paper. It consists of a complete solution, which contains the automated analysis of service protocols to identify qualification needs, the involvement of service technicians in the generation of learning materials, the preparation, transmission as well as the further development of those materials in accordance with the principles of e-learning. Finally, the solution contains a gamified mobile application for qualification, which is designed to meet the individual learning needs of the service technicians. This concept paper follows a problem-centred approach. Based on the current state of technology and research, the problem and motivation are identified and the urgency is verified. Furthermore, a detailed specification of the solution and a first implementation approach is presented.

  15. The Concept of Resource Use Efficiency as a Theoretical Basis for Promising Coal Mining Technologies

    Science.gov (United States)

    Mikhalchenko, Vadim

    2017-11-01

    The article is devoted to solving one of the most relevant problems of the coal mining industry - its high resource use efficiency, which results in high environmental and economic costs of operating enterprises. It is shown that it is the high resource use efficiency of traditional, historically developed coal production systems that generates a conflict between indicators of economic efficiency and indicators of resistance to uncertainty and variability of market environment parameters. The traditional technological paradigm of exploitation of coal deposits also predetermines high, technology-driven, economic risks. The solution is shown and a real example of the problem solution is considered.

  16. Language and Text-to-Speech Technologies for Highly Accessible Language & Culture Learning

    Directory of Open Access Journals (Sweden)

    Anouk Gelan

    2011-06-01

    Full Text Available This contribution presents the results of the “Speech technology integrated learning modules for Intercultural Dialogue” project. The project objective was to increase the availability and quality of e-learning opportunities for less widely-used and less taught European languages using a user-friendly and highly accessible learning environment. The integration of new Text-to-Speech developments into web-based authoring software for tutorial CALL had a double goal: on the one hand increase the accessibility of e-learning packages, also for learners having difficulty reading (e.g. dyslexic learners or preferring auditory learning; on the other hand exploiting some didactic possibilities of this technology.

  17. A CTD?Pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug?disease and drug?phenotype interactions

    OpenAIRE

    Davis, Allan Peter; Wiegers, Thomas C.; Roberts, Phoebe M; King, Benjamin L.; Lay, Jean M.; Lennon-Hopkins, Kelley; Sciaky, Daniela; Johnson, Robin; Keating, Heather; Greene, Nigel; hernandez, Robert; McConnell, Kevin J.; Enayetallah, Ahmed E.; Mattingly, Carolyn J.

    2013-01-01

    Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88 ...

  18. Evaluation of the strengths and weaknesses of Text Mining and Netnography as methods of understanding consumer conversations around luxury brands on social media platforms.

    OpenAIRE

    SAINI, CHITRA; ,

    2015-01-01

    The advent of social media has led to Luxury brands increasingly turning to social media sites to build brand value. Understanding the discussions that happen on social media is therefore a key for the marketing managers of luxury brands. There are two prominent methodologies which have been used widely in the literature to study consumer conversations on social media, these two methodologies are Text Mining and Netnography. In this study I will compare and contrast both these methodologies t...

  19. Expressive visual text-to-speech as an assistive technology for individuals with autism spectrum conditions

    OpenAIRE

    Cassidy, S A; Stenger, B.; Van Dongen, L.; Yanagisawa, K.; Anderson, R; Wan, V.; Baron-Cohen, S; Cipolla, R.

    2016-01-01

    Adults with Autism Spectrum Conditions (ASC) experience marked difficulties in recognising the emotions of others and responding appropriately. The clinical characteristics of ASC mean that face to face or group interventions may not be appropriate for this clinical group. This article explores the potential of a new interactive technology, converting text to emotionally expressive speech, to improve emotion processing ability and attention to faces in adults with ASC. We demonstrate a method...

  20. Statement of capabilities: Micropower Impulse Radar (MIR) technology applied to mine detection and imaging

    Energy Technology Data Exchange (ETDEWEB)

    Azevedo, S.G.; Gavel, D.T.; Mast, J.E.; Warhus, J.P.

    1995-03-13

    The Lawrence Livermore National Laboratory (LLNL) has developed radar and imaging technologies with potential applications in mine detection by the armed forces and other agencies involved in demining efforts. These new technologies use a patented ultra-wideband (impulse) radar technology that is compact, low-cost, and low power. Designated as Micropower Impulse Radar, these compact, self-contained radars can easily be assembled into arrays to form complete ground penetrating radar imaging systems. LLNL has also developed tomographic reconstruction and signal processing software capable of producing high-resolution 2-D and 3-D images of objects buried in materials like soil or concrete from radar data. Preliminary test results have shown that a radar imaging system using these technologies has the ability to image both metallic and plastic land mine surrogate targets buried in 5 to 10 cm of moist soil. In dry soil, the system can detect buried objects to a depth of 30 cm and more. This report describes LLNL`s unique capabilities and technologies that can be applied to the demining problem.

  1. Exploratory analysis of textual data from the Mother and Child Handbook using a text mining method (II): Monthly changes in the words recorded by mothers.

    Science.gov (United States)

    Tagawa, Miki; Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Ohwada, Michitaka; Matsubara, Shigeki

    2017-01-01

    The aim of the study was to examine the possibility of converting subjective textual data written in the free column space of the Mother and Child Handbook (MCH) into objective information using text mining and to compare any monthly changes in the words written by the mothers. Pregnant women without complications (n = 60) were divided into two groups according to State-Trait Anxiety Inventory grade: low trait anxiety (group I, n = 39) and high trait anxiety (group II, n = 21). Exploratory analysis of the textual data from the MCH was conducted by text mining using the Word Miner software program. Using 1203 structural elements extracted after processing, a comparison of monthly changes in the words used in the mothers' comments was made between the two groups. The data was mainly analyzed by a correspondence analysis. The structural elements in groups I and II were divided into seven and six clusters, respectively, by cluster analysis. Correspondence analysis revealed clear monthly changes in the words used in the mothers' comments as the pregnancy progressed in group I, whereas the association was not clear in group II. The text mining method was useful for exploratory analysis of the textual data obtained from pregnant women, and the monthly change in the words used in the mothers' comments as pregnancy progressed differed according to their degree of unease. © 2016 Japan Society of Obstetrics and Gynecology.

  2. Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER)system.

    Science.gov (United States)

    Pandey, Abhishek; Kreimeyer, Kory; Foster, Matthew; Botsis, Taxiarchis; Dang, Oanh; Ly, Thomas; Wang, Wei; Forshee, Richard

    2018-01-01

    Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

  3. Position paper: cognitive radio networking for multiple sensor network interoperability in mines

    CSIR Research Space (South Africa)

    Kagize, BM

    2008-01-01

    Full Text Available This paper proposes the use of cognitive radio technology for multiple wireless sensor technologies in mines. The work is motivated by the lack of flexible and scalable sensor networks in mines. The proposed architecture uses cognitive radio...

  4. Life cycle Greenhouse gas emissions of current Oil Sands Technologies: surface mining and in situ applications.

    Science.gov (United States)

    Bergerson, Joule A; Kofoworola, Oyeshola; Charpentier, Alex D; Sleep, Sylvia; Maclean, Heather L

    2012-07-17

    Life cycle greenhouse gas (GHG) emissions associated with two major recovery and extraction processes currently utilized in Alberta's oil sands, surface mining and in situ, are quantified. Process modules are developed and integrated into a life cycle model-GHOST (GreenHouse gas emissions of current Oil Sands Technologies) developed in prior work. Recovery and extraction of bitumen through surface mining and in situ processes result in 3-9 and 9-16 g CO(2)eq/MJ bitumen, respectively; upgrading emissions are an additional 6-17 g CO(2)eq/MJ synthetic crude oil (SCO) (all results are on a HHV basis). Although a high degree of variability exists in well-to-wheel emissions due to differences in technologies employed, operating conditions, and product characteristics, the surface mining dilbit and the in situ SCO pathways have the lowest and highest emissions, 88 and 120 g CO(2)eq/MJ reformulated gasoline. Through the use of improved data obtained from operating oil sands projects, we present ranges of emissions that overlap with emissions in literature for conventional crude oil. An increased focus is recommended in policy discussions on understanding interproject variability of emissions of both oil sands and conventional crudes, as this has not been adequately represented in previous studies.

  5. Application of indirect stress measurement techniques (non strain gauge based technology) to quantify stress environments in mines

    CSIR Research Space (South Africa)

    Stacey, TR

    2002-03-01

    Full Text Available Reliable values of in situ stress are essential for the valid modelling of mine layouts. Available non-strain gauge methods are reviewed as potential practical techniques for South African mines. From this review it is concluded that the most...

  6. Expressive visual text-to-speech as an assistive technology for individuals with autism spectrum conditions.

    Science.gov (United States)

    Cassidy, S A; Stenger, B; Van Dongen, L; Yanagisawa, K; Anderson, R; Wan, V; Baron-Cohen, S; Cipolla, R

    2016-07-01

    Adults with Autism Spectrum Conditions (ASC) experience marked difficulties in recognising the emotions of others and responding appropriately. The clinical characteristics of ASC mean that face to face or group interventions may not be appropriate for this clinical group. This article explores the potential of a new interactive technology, converting text to emotionally expressive speech, to improve emotion processing ability and attention to faces in adults with ASC. We demonstrate a method for generating a near-videorealistic avatar (XpressiveTalk), which can produce a video of a face uttering inputted text, in a large variety of emotional tones. We then demonstrate that general population adults can correctly recognize the emotions portrayed by XpressiveTalk. Adults with ASC are significantly less accurate than controls, but still above chance levels for inferring emotions from XpressiveTalk. Both groups are significantly more accurate when inferring sad emotions from XpressiveTalk compared to the original actress, and rate these expressions as significantly more preferred and realistic. The potential applications for XpressiveTalk as an assistive technology for adults with ASC is discussed.

  7. Development of Test Rig for Robotization of Mining Technological Processes – Oversized Rock Breaking Process Case

    Science.gov (United States)

    Pawel, Stefaniak; Jacek, Wodecki; Jakubiak, Janusz; Zimroz, Radoslaw

    2017-12-01

    Production chain (PCh) in underground copper ore mine consists of several subprocesses. From our perspective implementation of so called ZEPA approach (Zero Entry Production Area) might be very interesting [16]. In practice, it leads to automation/robotization of subprocesses in production area. In this paper was investigated a specific part of PCh i.e. a place when cyclic transport by LHDs is replaced with continuous transport by conveying system. Such place is called dumping point. The objective of dumping points with screen is primary classification of the material (into coarse and fine material) and breaking oversized rocks with hydraulic hammer. Current challenges for the underground mining include e.g. safety improvement as well as production optimization related to bottlenecks, stoppages and operational efficiency of the machines. As a first step, remote control of the hydraulic hammer has been introduced, which not only transferred the operator to safe workplace, but also allowed for more comfortable work environment and control over multiple technical objects by a single person. Today literature analysis shows that current mining industry around the world is oriented to automation and robotization of mining processes and reveals technological readiness for 4th industrial revolution. The paper is focused on preliminary analysis of possibilities for the use of the robotic system to rock-breaking process. Prototype test rig has been proposed and experimental works have been carried out. Automatic algorithms for detection of oversized rocks, crushing them as well as sweeping and loosening of material have been formulated. Obviously many simplifications have been assumed. Some near future works have been proposed.

  8. Service mining framework and application

    CERN Document Server

    Chang, Wei-Lun

    2014-01-01

    The shifting focus of service from the 1980s to 2000s has proved that IT not only lowers the cost of service but creates avenues to enhance and increase revenue through service. The new type of service, e-service, is mobile, flexible, interactive, and interchangeable. While service science provides an avenue for future service researches, the specific research areas from the IT perspective still need to be elaborated. This book introduces a novel concept-service mining-to address several research areas from technology, model, management, and application perspectives. Service mining is defined as "a systematical process including service discovery, service experience, service recovery, and service retention to discover unique patterns and exceptional values within the existing services." The goal of service mining is similar to data mining, text mining, or web mining, and aims to "detect something new" from the service pool. The major difference is the feature of service is quite distinct from the mining targe...

  9. Hybrid Technology of Hard Coal Mining from Seams Located at Great Depths

    Science.gov (United States)

    Czaja, Piotr; Kamiński, Paweł; Klich, Jerzy; Tajduś, Antoni

    2014-10-01

    Learning to control fire changed the life of man considerably. Learning to convert the energy derived from combustion of coal or hydrocarbons into another type of energy, such as steam pressure or electricity, has put him on the path of scientific and technological revolution, stimulating dynamic development. Since the dawn of time, fossil fuels have been serving as the mankind's natural reservoir of energy in an increasingly great capacity. A completely incomprehensible refusal to use fossil fuels causes some local populations, who do not possess a comprehensive knowledge of the subject, to protest and even generate social conflicts as an expression of their dislike for the extraction of minerals. Our times are marked by the search for more efficient ways of utilizing fossil fuels by introducing non-conventional technologies of exploiting conventional energy sources. During apartheid, South Africa demonstrated that cheap coal can easily satisfy total demand for liquid and gaseous fuels. In consideration of current high prices of hydrocarbon media (oil and gas), gasification or liquefaction of coal seems to be the innovative technology convergent with contemporary expectations of both energy producers as well as environmentalists. Known mainly from literature reports, underground coal gasification technologies can be brought down to two basic methods: - shaftless method - drilling, in which the gasified seam is uncovered using boreholes drilled from the surface, - shaft method, in which the existing infrastructure of underground mines is used to uncover the seams. This paper presents a hybrid shaft-drilling approach to the acquisition of primary energy carriers (methane and syngas) from coal seams located at great depths. A major advantage of this method is the fact that the use of conventional coal mining technology requires the seams located at great depths to be placed on the off-balance sheet, while the hybrid method of underground gasification enables them to

  10. Preliminary research in using the technology of data mining to analyze remote sensing data

    Science.gov (United States)

    Zhang, Xiongfei; Li, Xing; Zhang, Xia; Tong, Qingxi; Liu, Wei; Zheng, Lanfen

    2003-09-01

    Remote sensing data, especially the hyperspectral remote sensing data, characterize their great quantities. So how to deal wtih these data is a focus. Database has solved the problem of storing, searching, updating and maintaining of the data, but it is not satisfactory in disposing them. In recent years, the technology of data warehouse has great development. It can re-integrate, synthesize and separate the data of database, and use the searching pattern of multiple dimensions to realize data mining (DM). This technolgoy has been widely used in commerce to analyze the inner relationship of the numerous data and makes some remarkable achievements in decision supporting. Data warehouse and Data mining technology have been used in GIS. This article would give a set of complete steps and some general methods in using the DM to analyze the remote sensing data, especially in hyperspectral data. And it tries to do some preliminary exploration in using it to deeply analyze the potential relations among the acquired spectra, images and biology parameters of the experiments and get some anticipated possible results.

  11. Airborne testing of the joint mine detection technology's tunable filter multispectral camera

    Science.gov (United States)

    Crosby, Frank J.; Holloway, John H., Jr.; Petee, Danny A.; Stetson, Suzanne P.; Suiter, Harold R.; Tinsley, Ken R.

    2002-08-01

    The Joint Mine Detection Technology (JMDT) project, following successful field-based testing of its new Tunable Filter Multispectral Camera (TFMC) has now completed initial Airborne Testing of the TFMC at both the Coastal Systems Station and Eglin Air Force Base sites. An overview of the testing is presented along with the investigations into the advantages of a system utilizing the TFMC in airborne operational scenarios. The TFMC-like tuning flexibility was flight-tested using optimized wavelength combinations, which were found using field test data, over a variety of backgrounds and altitudes. The data revealed the suitability of background tuning, polarization, and mechanically co-registered channels as benefits to multispectral target detection. The data were also compared to that collected with an IMC-201 camera, using the six filters of the Coastal Battlefield Reconnaissance and Analysis (COBRA) Advanced Technology Demonstration (ATD) system, in order to determine improvements over existing capabilities.

  12. Use of the Rotary-Percussion Drilling Technology in the OKR Mining Conditions and by Drilling the Wells for Purposes of Heating Pump Installation

    Directory of Open Access Journals (Sweden)

    Koníèek Jiøí

    2004-09-01

    Full Text Available Sedimentary rocks in the Ostrava-Karviná Coalfield (OKR especially the Saddle Seams of the Karviná Formation are marked with considerable abrasive property following from the high content of siliceous component in these rocks. In these conditions drilling, using the classic methods seems to be economically unefficient.Some experiences from use of the new method – rotary-percussion drilling technology, using downhole drills and tested by OKD, DPB s.c. Paskov under various conditions in the OKR mines and also from drilling the wells for heating pump fitting commonly with the proposal of the innovation of this technology are presented in this paper.

  13. Using Bitmap Indexing Technology for Combined Numerical and TextQueries

    Energy Technology Data Exchange (ETDEWEB)

    Stockinger, Kurt; Cieslewicz, John; Wu, Kesheng; Rotem, Doron; Shoshani, Arie

    2006-10-16

    In this paper, we describe a strategy of using compressedbitmap indices to speed up queries on both numerical data and textdocuments. By using an efficient compression algorithm, these compressedbitmap indices are compact even for indices with millions of distinctterms. Moreover, bitmap indices can be used very efficiently to answerBoolean queries over text documents involving multiple query terms.Existing inverted indices for text searches are usually inefficient forcorpora with a very large number of terms as well as for queriesinvolving a large number of hits. We demonstrate that our compressedbitmap index technology overcomes both of those short-comings. In aperformance comparison against a commonly used database system, ourindices answer queries 30 times faster on average. To provide full SQLsupport, we integrated our indexing software, called FastBit, withMonetDB. The integrated system MonetDB/FastBit provides not onlyefficient searches on a single table as FastBit does, but also answersjoin queries efficiently. Furthermore, MonetDB/FastBit also provides avery efficient retrieval mechanism of result records.

  14. FROM DATA MINING TO BEHAVIOR MINING

    OpenAIRE

    ZHENGXIN CHEN

    2006-01-01

    Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of complex data (as exemplified by graph mining, text mining, etc.). By incorporating the relationships of the data along with the data itself (rathe...

  15. Exploration and Mining Roadmap

    Energy Technology Data Exchange (ETDEWEB)

    none,

    2002-09-01

    This Exploration and Mining Technology Roadmap represents the third roadmap for the Mining Industry of the Future. It is based upon the results of the Exploration and Mining Roadmap Workshop held May 10 ñ 11, 2001.

  16. Diversification of the Higher Mining Education Financing in Globalization Era

    Directory of Open Access Journals (Sweden)

    Frolova Victoria

    2017-01-01

    Full Text Available In the current conditions of global competition, the development of new mining technologies, the requirements to labor resources, their skills and creative potential are increasing. The tasks facing the mining industry cannot be solved without highly qualified personnel, especially managers, engineers and technicians, specialists who possess the knowledge and competences necessary for the development of science and technology of mining, and ensuring mining industrial safety. The authors analyze personnel problems and financing of mining higher education, conclude that there is a need to develop social partnership and diversify the sources of funding for training, advanced training and retraining of personnel for mining and processing of solid mineral deposits.

  17. An Electronic Market Space Architecture Based On Intelligent Agents And Data Mining Technologies

    Directory of Open Access Journals (Sweden)

    2007-01-01

    Full Text Available This paper presents an overview of current trends in electronic Business (E-Business, and discusses how an enterprise can use the Electronic Market space based on intelligent agents and data mining techniques to its strategic advantage. We define an agency as a multi-agent system created by integrating agents, selected from a library of reusable agents that have formed a federation. A federation of agents comprises of a set of registered agents, witch are themselves complete knowledge-based system [1].

  18. THE CYANIDE LEACH TECHNOLOGY IN GOLD AND SILVER MINING HARMFUL EFFECTS AND SUGGESTIONS FOR REMEDYING

    Directory of Open Access Journals (Sweden)

    Judit PETRES

    2013-11-01

    Full Text Available The region named Roşia Montană became famous due to its rich mineral resources. The most precious treasure which has been found is the gold. A state-run gold mine functioned for many years and the continuous pollution due to the gold extraction became very dangerous. All these cyanide-containing waste are hazardous to wildlife, natural resources and human health if not properly managed. The consequences of cyanide hazards may be devastating, sometimes killing everything for several miles downstream. It has not yet been found a way of both taking advantages of the economic benefits of gold and maintaining a liveable habitat.

  19. When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.

    Directory of Open Access Journals (Sweden)

    Young Bin Kim

    Full Text Available Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.

  20. Prodromal signs and symptoms of serious infections with tocilizumab treatment for rheumatoid arthritis: Text mining of the Japanese postmarketing adverse event-reporting database.

    Science.gov (United States)

    Atsumi, Tatsuya; Ando, Yoshiaki; Matsuda, Shinichi; Tomizawa, Shiho; Tanaka, Riwa; Takagi, Nobuhiro; Nakasone, Ayako

    2017-09-07

    To search for signs and symptoms before serious infection (SI) occurs in tocilizumab (TCZ)-treated rheumatoid arthritis (RA) patients. Individual case safety reports, including structured (age, sex, adverse event [AE]) and unstructured (clinical narratives) data, were analyzed by automated text mining from a Japanese post-marketing AE-reporting database (16 April 2008-10 April 2015) assuming the following: treated in Japan; TCZ RA treatment; ≥1 SI; unable to exclude causality between TCZ and SIs. The database included 7653 RA patients; 1221 reports met four criteria, encompassing 1591 SIs. Frequent SIs were pneumonia (15.9%), cellulitis (9.9%), and sepsis (5.0%). Reports for 782 patients included SI onset date; 60.7% of patients had signs/symptoms ≤28 days before SI diagnosis, 32.7% had signs/symptoms with date unidentified, 1.7% were asymptomatic, and 4.9% had unknown signs/symptoms. The most frequent signs/symptoms were for skin (swelling and pain) and respiratory (cough and pyrexia) infections. Among 68 patients who had normal laboratory results for C-reactive protein, body temperature, and white blood cell count, 94.1% had signs or symptoms of infection. This study identified prodromal signs and symptoms of SIs in RA patients receiving TCZ. Data mining clinical narratives from post-marketing AE databases may be beneficial in characterizing SIs.

  1. PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries

    Directory of Open Access Journals (Sweden)

    Barbosa-Silva Adriano

    2011-11-01

    Full Text Available Abstract Background Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. Results To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer. Conclusions PESCADOR is a platform independent web resource available at: http://cbdm.mdc-berlin.de/tools/pescador/

  2. Study of Cloud Based ERP Services for Small and Medium Enterprises (Data is Processed by Text Mining Technique

    Directory of Open Access Journals (Sweden)

    SHARMA, R.

    2014-06-01

    Full Text Available The purpose of this research paper is to explore the knowledge of the existing studies related to cloud computing current trend. The outcome of research is demonstrated in the form of diagram which simplifies the ERP integration process for in-house and cloud eco-system. It will provide a conceptual view to the new client or entrepreneurs using ERP services and explain them how to deal with two stages of ERP systems (cloud and in-house. Also suggest how to improve knowledge about ERP services and implementation process for both stages. The work recommends which ERP services can be outsourced over the cloud. Cloud ERP is a mix of standard ERP services along with cloud flexibility and low cost to afford these services. This is a recent phenomenon in enterprise service offering. For most of non IT background entrepreneurs it is unclear and broad concept, since all the research work related to it are done in couple of years. Most of cloud ERP vendors describe their products as straight forward tasks. The process and selection of Cloud ERP Services and vendors is not clear. This research work draws a framework for selecting non-core business process from preferred ERP service partners. It also recommends which ERP services outsourced first over the cloud, and the security issues related to data or information moved out from company premises to the cloud eco-system.

  3. Benefits of off-campus education for students in the health sciences: a text-mining analysis.

    Science.gov (United States)

    Nakagawa, Kazumasa; Asakawa, Yasuyoshi; Yamada, Keiko; Ushikubo, Mitsuko; Yoshida, Tohru; Yamaguchi, Haruyasu

    2012-08-28

    In Japan, few community-based approaches have been adopted in health-care professional education, and the appropriate content for such approaches has not been clarified. In establishing community-based education for health-care professionals, clarification of its learning effects is required. A community-based educational program was started in 2009 in the health sciences course at Gunma University, and one of the main elements in this program is conducting classes outside school. The purpose of this study was to investigate using text-analysis methods how the off-campus program affects students. In all, 116 self-assessment worksheets submitted by students after participating in the off-campus classes were decomposed into words. The extracted words were carefully selected from the perspective of contained meaning or content. With the selected terms, the relations to each word were analyzed by means of cluster analysis. Cluster analysis was used to select and divide 32 extracted words into four clusters: cluster 1-"actually/direct," "learn/watch/hear," "how," "experience/participation," "local residents," "atmosphere in community-based clinical care settings," "favorable," "communication/conversation," and "study"; cluster 2-"work of staff member" and "role"; cluster 3-"interaction/communication," "understanding," "feel," "significant/important/necessity," and "think"; and cluster 4-"community," "confusing," "enjoyable," "proactive," "knowledge," "academic knowledge," and "class." The students who participated in the program achieved different types of learning through the off-campus classes. They also had a positive impression of the community-based experience and interaction with the local residents, which is considered a favorable outcome. Off-campus programs could be a useful educational approach for students in health sciences.

  4. Using chat and text technologies to answer sexual and reproductive health questions: Planned Parenthood pilot study.

    Science.gov (United States)

    Giorgio, Margaret M; Kantor, Leslie M; Levine, Deborah S; Arons, Whitney

    2013-09-20

    Teens and young adults in the United States are in need of sexual and reproductive health information, as evidenced by elevated rates of sexually transmitted infections (STIs), pregnancy, and births among this population. In-person sexuality education programs are helpful, but they are unlikely to rapidly accommodate teens and young adults in a moment of crisis. Evidence suggests that technologies such as instant messaging (IM) and text messaging may be effective ways to provide teens and young adults with sexual and reproductive health information. In September 2010, Planned Parenthood Federation of America launched a text and IM program designed to provide immediate answers to urgent sexual and reproductive health questions from a reliable and confidential source and to link young people to sexual and reproductive health services if needed. To assess whether this program is successful in reaching the target population, whether user characteristics vary by mode (IM vs text), and whether mode is associated with reaching individuals with high levels of worry or reducing worry postchat. Data were collected from prechat and postchat surveys for all IM and text message conversations between September 2010 and August 2011. A bivariate analysis was conducted using chi-square tests for differences in the main covariates by mode of conversation. In the multivariable analysis, logistic regression was used to identify factors that were independently associated with prechat levels of worry and changes in worry postchat. A total of 32,589 conversations occurred during the program's first year. The odds of feeling very worried prechat were highest for IM users (adjusted odds ratio [AOR] 1.43, 95% CI 1.20-1.72), users 17 years and younger (AOR 1.62, 95% CI 1.50-1.74), Latino/Hispanic users (AOR 1.36, 95% CI 1.27-1.46), and black users (AOR 1.40, 95% CI 1.30-1.50). After controlling for the study covariates, there was no significant difference in the odds of feeling better (less

  5. The Contribution of the Vaccine Adverse Event Text Mining System to the Classification of Possible Guillain-Barré Syndrome Reports

    Science.gov (United States)

    Botsis, T.; Woo, E. J.; Ball, R.

    2013-01-01

    Background We previously demonstrated that a general purpose text mining system, the Vaccine adverse event Text Mining (VaeTM) system, could be used to automatically classify reports of an-aphylaxis for post-marketing safety surveillance of vaccines. Objective To evaluate the ability of VaeTM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS). Methods We used VaeTM to extract the key diagnostic features from the text of reports in VAERS. Then, we applied the Brighton Collaboration (BC) case definition for GBS, and an information retrieval strategy (i.e. the vector space model) to quantify the specific information that is included in the key features extracted by VaeTM and compared it with the encoded information that is already stored in VAERS as Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs). We also evaluated the contribution of the primary (diagnosis and cause of death) and secondary (second level diagnosis and symptoms) diagnostic VaeTM-based features to the total VaeTM-based information. Results MedDRA captured more information and better supported the classification of reports for GBS than VaeTM (AUC: 0.904 vs. 0.777); the lower performance of VaeTM is likely due to the lack of extraction by VaeTM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the VaeTM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the VaeTM-based information was contained in the secondary diagnostic features. Conclusion For GBS, clinical signs and symptoms alone are not sufficient to match MedDRA coding for purposes of case classification, but are preferred if specificity is the priority. PMID:23650490

  6. Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques.

    Science.gov (United States)

    Magua, Wairimu; Zhu, Xiaojin; Bhattacharya, Anupama; Filut, Amarette; Potvien, Aaron; Leatherberry, Renee; Lee, You-Geon; Jens, Madeline; Malikireddy, Dastagiri; Carnes, Molly; Kaatz, Anna

    2017-05-01

    Women are less successful than men in renewing R01 grants from the National Institutes of Health. Continuing to probe text mining as a tool to identify gender bias in peer review, we used algorithmic text mining and qualitative analysis to examine a sample of critiques from men's and women's R01 renewal applications previously analyzed by counting and comparing word categories. We analyzed 241 critiques from 79 Summary Statements for 51 R01 renewals awarded to 45 investigators (64% male, 89% white, 80% PhD) at the University of Wisconsin-Madison between 2010 and 2014. We used latent Dirichlet allocation to discover evaluative "topics" (i.e., words that co-occur with high probability). We then qualitatively examined the context in which evaluative words occurred for male and female investigators. We also examined sex differences in assigned scores controlling for investigator productivity. Text analysis results showed that male investigators were described as "leaders" and "pioneers" in their "fields," with "highly innovative" and "highly significant research." By comparison, female investigators were characterized as having "expertise" and working in "excellent" environments. Applications from men received significantly better priority, approach, and significance scores, which could not be accounted for by differences in productivity. Results confirm our previous analyses suggesting that gender stereotypes operate in R01 grant peer review. Reviewers may more easily view male than female investigators as scientific leaders with significant and innovative research, and score their applications more competitively. Such implicit bias may contribute to sex differences in award rates for R01 renewals.

  7. System catalytic neutralization control of combustion engines waste gases in mining technologies

    Science.gov (United States)

    Korshunov, G. I.; Solnitsev, R. I.

    2017-10-01

    The paper presents the problems solution of the atmospheric air pollution with the exhaust gases of the internal combustion engines, used in mining technologies. Such engines are used in excavators, bulldozers, dump trucks, diesel locomotives in loading and unloading processes and during transportation of minerals. NOx, CO, CH emissions as the waste gases occur during engine operation, the concentration of which must be reduced to the standard limits. The various methods and means are used for the problem solution, one of which is neutralization based on platinum catalysts. A mathematical model of a controlled catalytic neutralization system is proposed. The simulation results confirm the increase in efficiency at start-up and low engine load and the increase in the catalyst lifetime.

  8. Review of Speech-to-Text Recognition Technology for Enhancing Learning

    National Research Council Canada - National Science Library

    Rustam Shadiev; Wu-Yuin Hwang; Nian-Shing Chen; Yueh-Min Huang

    2014-01-01

    ...) technology has been applied to enhance learning. The first aim of this review is to understand how STR technology has been used to support learning over the past fifteen years, and the second is to analyze all research evidence to understand...

  9. Analysis of ingredient lists of commercially available gluten-free and gluten-containing food products using the text mining technique.

    Science.gov (United States)

    do Nascimento, Amanda Bagolin; Fiates, Giovanna Medeiros Rataichesck; Dos Anjos, Adilson; Teixeira, Evanilda

    2013-03-01

    Ingredients mentioned on the labels of commercially available packaged gluten-free and similar gluten-containing food products were analyzed and compared, using the text mining technique. A total of 324 products' labels were analyzed for content (162 from gluten-free products), and ingredient diversity in gluten-free products was 28% lower. Raw materials used as ingredients of gluten-free products were limited to five varieties: rice, cassava, corn, soy, and potato. Sugar was the most frequently mentioned ingredient on both types of products' labels. Salt and sodium also were among these ingredients. Presence of hydrocolloids, enzymes or raw materials of high nutritional content such as pseudocereals, suggested by academic studies as alternatives to improve nutritional and sensorial quality of gluten-free food products, was not identified in the present study. Nutritional quality of gluten-free diets and health of celiac patients may be compromised.

  10. SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data.

    Science.gov (United States)

    Venkatesan, Aravind; Kim, Jee-Hyub; Talo, Francesco; Ide-Smith, Michele; Gobeill, Julien; Carter, Jacob; Batista-Navarro, Riza; Ananiadou, Sophia; Ruch, Patrick; McEntyre, Johanna

    2016-01-01

    The tremendous growth in biological data has resulted in an increase in the number of research papers being published. This presents a great challenge for scientists in searching and assimilating facts described in those papers. Particularly, biological databases depend on curators to add highly precise and useful information that are usually extracted by reading research articles. Therefore, there is an urgent need to find ways to improve linking literature to the underlying data, thereby minimising the effort in browsing content and identifying key biological concepts.   As part of the development of Europe PMC, we have developed a new platform, SciLite, which integrates text-mined annotations from different sources and overlays those outputs on research articles. The aim is to aid researchers and curators using Europe PMC in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data.

  11. SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data

    Science.gov (United States)

    Talo, Francesco; Ide-Smith, Michele; Gobeill, Julien; Carter, Jacob; Batista-Navarro, Riza; Ananiadou, Sophia; Ruch, Patrick; McEntyre, Johanna

    2017-01-01

    The tremendous growth in biological data has resulted in an increase in the number of research papers being published. This presents a great challenge for scientists in searching and assimilating facts described in those papers. Particularly, biological databases depend on curators to add highly precise and useful information that are usually extracted by reading research articles. Therefore, there is an urgent need to find ways to improve linking literature to the underlying data, thereby minimising the effort in browsing content and identifying key biological concepts.   As part of the development of Europe PMC, we have developed a new platform, SciLite, which integrates text-mined annotations from different sources and overlays those outputs on research articles. The aim is to aid researchers and curators using Europe PMC in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data. PMID:28948232

  12. A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.

    Science.gov (United States)

    Davis, Allan Peter; Wiegers, Thomas C; Roberts, Phoebe M; King, Benjamin L; Lay, Jean M; Lennon-Hopkins, Kelley; Sciaky, Daniela; Johnson, Robin; Keating, Heather; Greene, Nigel; Hernandez, Robert; McConnell, Kevin J; Enayetallah, Ahmed E; Mattingly, Carolyn J

    2013-01-01

    Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88,629 articles relating over 1,200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 254,173 toxicogenomic interactions (152,173 chemical-disease, 58,572 chemical-gene, 5,345 gene-disease and 38,083 phenotype interactions). All chemical-gene-disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer's text-mining process to collate the articles, and CTD's curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug-disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades' worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/

  13. Pressure-relief and methane production performance of pressure relief gas extraction technology in the longwall mining

    Science.gov (United States)

    Zhang, Cun; Tu, Shihao; Chen, Min; Zhang, Lei

    2017-02-01

    Pressure relief gas extraction technology (PRGET) has been successfully implemented at many locations as a coal mine methane exploitation and outburst prevention technology. Comprehensive PRGET including gob gas venthole (GGV), crossing seam drilling hole (CSDH), large diameter horizontal long drilling hole (LDHLDH) and buried pipe for extraction (BPE) have been used to extract abundant pressure-relief methane (PRM) during protective coal seam mining; these techniques mitigated dangers associated with coal and gas outbursts in 13-1 coal seam mining in the Huainan coalfield. These extraction technologies can ensure safe protective seam mining and effectively extract coal and gas. This article analyses PRGET production performance and verifies it with the field measurement. The results showed that PRGET drilling to extract PRM from the protected coal seam significantly reduced methane emissions from a longwall ventilation system and produced highly efficient extraction. Material balance analyses indicated a significant decrease in gas content and pressure in the protected coal seam, from 8.78 m3 t-1 and 4.2 MPa to 2.34 m3 t-1 and 0.285 MPa, respectively. The field measurement results of the residual gas content in protected coal seam (13-1 coal seam) indicated the reliability of the material balance analyses and the pressure relief range of PRGET in the protected coal seam is obtained.

  14. [Acupoints selection rules analysis of ancient acupuncture for urinary incontinence based on data mining technology].

    Science.gov (United States)

    Zhang, Wei; Tan, Zhigao; Cao, Juanshu; Gong, Houwu; Qin, Zuoai; Zhong, Feng; Cao, Yue; Wei, Yanrong

    2015-12-01

    Based on ancient literature of acupuncture in Canon of Chinese Medicine (4th edition), the articles regarding acupuncture for urinary incontinence were retrieved and collected to establish a database. By Weka data mining software, the multi-level association rules analysis method was applied to analyze the acupoints selection characteristics and rules of ancient acupuncture for treatment of urinary incontinence. Totally 356 articles of acupuncture for urinary incontinence were collected, involving 41 acupoints with a total frequency of 364. As a result, (1) the acupoints in the yin-meridian of hand and foot were highly valued, as the frequency of acupoints in yin-meridians was 2.6 times than that in yang-meridians, and the frequency of acupoints selected was the most in the liver meridian of foot-jueyin; (2) the acupoints in bladder meridian of foot-taiyang were also highly valued, and among three yang-meridians of foot, the frequency of acupoints in the bladder meridian of foot-taiyang was 54, accounting for 65.85% (54/82); (3) more acupoints selected were located in the lower limbs and abdomen; (4) specific acupoints in above meridians were mostly selected, presenting 73.2% (30/41) to the ratio of number and 79.4% (289/364) to the frequency, respectively; (5) Zhongji (CV 3), the front-mu point of bladder meridian, was seldom selected in the ancient acupuncture literature, which was different from modern literature reports. The results show that urinary incontinence belongs to external genitalia diseases, which should be treated from yin, indicating more yin-meridians be used and special acupoints be focused on. It is essential to focus inheritance and innovation in TCM clinical treatment, and applying data mining technology to ancient literature of acupuncture could provide classic theory basis for TCM clinical treatment.

  15. Philippine Mining Capitalism: The Changing Terrains of Struggle in the Neoliberal Mining Regime

    Directory of Open Access Journals (Sweden)

    Alvin A. Camba

    2016-06-01

    Full Text Available This article analyzes how the mining sector and anti-mining groups compete for mining outcomes in the Philippines. I argue that the transition to a neoliberal mineral regime has empowered the mining sector and weakened the mining groups by shifting the terrains of struggle onto the domains of state agencies and scientific networks. Since the neoliberal era, the mining sector has come up with two strategies. First, technologies of subjection elevate various public institutions to elect and select the processes aimed at making mining accountable and sensitive to the demands of local communities. However, they often refuse or lack the capacity to intervene effectively. Second, technologies of subjectivities allow a selective group of industry experts to single-handedly determine the environmental viability of mining projects. Mining consultants, specialists, and scientists chosen by mining companies determine the potential environmental damage on water bodies, air pollution, and soil erosion. Because of the mining capital’s access to economic and legal resources, anti-mining communities across the Philippines have been forced to compete on an unequal terrain for a meaningful social dialogue and mining outcomes.

  16. Effects of long-term coal supply contracts on technology adoption and improvements in the mining of coal. Final technical report

    Energy Technology Data Exchange (ETDEWEB)

    Walton, D.R.; Hawkins, S.A.; Webb, P.F.; Kauffman, P.W.

    1979-08-01

    The relationship between long-term coal supply contracts and the adoption of new technology in the coal mining industry is a complex one. From this study certain conclusions can be drawn. New technologies and improvements in the mining of coal can be logically categorized into three areas: evolutionary technology, transitional technology, or innovative technology. Evolutionary improvements in technology can be categorized as improvements, or increased production capacities, in existing equipment. Transitional technology involves the adoption of existing or proven technologies into new conditions, or, proceeding from one technology type to a newer type for the same function. Innovative technology includes equipment, concepts, and systems not readily available, or untried, in the existing mining environment (seam conditions, etc.). Technology adoption is an economic decision. This point was repeatedly emphasized by industry representatives contacted during the study. The long-term coal supply contract influences the decision to adopt new technology and mining improvements in several ways depending on the technology type (i.e., evolutionary, transitional, or innovative), and also the coal supplier type (i.e., captive or independent producer). Several examples of the adoption of new technologies in mines under long-term coal supply contracts are discussed. (LTN)

  17. A Case Study of Effective Support Working Resistance and Roof Support Technology in Thick Seam Fully-Mechanized Face Mining with Hard Roof Conditions

    Directory of Open Access Journals (Sweden)

    Wei-bin Guo

    2017-06-01

    Full Text Available This paper presents the engineering geological properties and roof control tecnology for a thick coal seam fully-mechanized face mining with hard roof conditions (THC at the Jinhuagong Coal Mine (JCM, northwest China. The effective support working resistance and appropriate roof control technology are two critical factors for safe and productive mining in the THC. The load-estimate-method (LOEM is the effective method to determine the support working resistance for normal working conditions (the mining height less than 3.5 m. In order to prevent support crushing accidents from happening and to ensure the safety and high-efficiency in the THC, the LOEM was modified based on the structure of the overlying strata in the THC. The strata which can form the voussoir beam structure in normal working conditions and will break in the form of cantilever beam in the THC is defined as the key strata in the immediate roof. Therefore, the hanging length of the key strata in the immediate roof was considered in the LOEM. Furthermore, a method for calculating the hanging length of the key strata in the immediate roof and its influencing factors were proposed using cantilever beam theory analysis of the structure of the overlying strata. Moreover, in order to fully fill the goaf area with caving roof to reduce the energy accumulation of main roof movement, it was decided to apply destress blasting technique (DEBT at the JCM to control the large hanging length of the hard roof, so as to reduce the impact of the hard main roof movement on the working face. The key technique parameters of the roof caving borehole were also proposed. The obtained results demonstrated that the theoretical analysis is reasonable, and the chosen support type and the DEBT could meet the roof control requirements. The THC has achieved safety and high-efficiency mining.

  18. Crossing Educational Boundaries: Text, Technology, and Dialogue as a Critical Pathway

    Science.gov (United States)

    Abnet, LaNae; Nichols, Joe D.; Moss, Glenda

    2008-01-01

    Can the perceived boundary between professor and student be crossed? Can technology be used as a pathway to cross that boundary? These questions were answered as a result of a project initiated to meet the requirements in creating an honors option for an undergraduate educational psychology course. In constructing the course requirements, the…

  19. Gaming, texting, learning? Teaching engineering ethics through students' lived experiences with technology.

    Science.gov (United States)

    Voss, Georgina

    2013-09-01

    This paper examines how young peoples' lived experiences with personal technologies can be used to teach engineering ethics in a way which facilitates greater engagement with the subject. Engineering ethics can be challenging to teach: as a form of practical ethics, it is framed around future workplace experience in a professional setting which students are assumed to have no prior experience of. Yet the current generations of engineering students, who have been described as 'digital natives', do however have immersive personal experience with digital technologies; and experiential learning theory describes how students learn ethics more successfully when they can draw on personal experience which give context and meaning to abstract theories. This paper reviews current teaching practices in engineering ethics; and examines young people's engagement with technologies including cell phones, social networking sites, digital music and computer games to identify social and ethical elements of these practices which have relevance for the engineering ethics curricula. From this analysis three case studies are developed to illustrate how facets of the use of these technologies can be drawn on to teach topics including group work and communication; risk and safety; and engineering as social experimentation. Means for bridging personal experience and professional ethics when teaching these cases are discussed. The paper contributes to research and curriculum development in engineering ethics education, and to wider education research about methods of teaching 'the net generation'.

  20. Tweets, Texts, and Tablets:The Emergence of Technology-Based Self-Monitoring

    Science.gov (United States)

    Bruhn, Allison Leigh; Waller, LaNeisha; Hasselbring, Ted S.

    2016-01-01

    Students with behavior problems often lack the self-regulation skills necessary for success. One strategy shown to improve these skills is self-monitoring. Traditionally, self-monitoring has been done using paper and pencil, with some sort of prompt to complete the procedures. Prompts have involved teacher cues as well as technology. Current…