Information Retrieval in Biomedical Research: From Articles to Datasets

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Wei, Wei

2017-01-01

Information retrieval techniques have been applied to biomedical research for a variety of purposes, such as textual document retrieval and molecular data retrieval. As biomedical research evolves over time, information retrieval is also constantly facing new challenges, including the growing number of available data, the emerging new data types,…

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

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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

Proposal for a new LEIR Slow Extraction Scheme dedicated to Biomedical Research

CERN Document Server

Garonna, A; Carli, C

2014-01-01

This report presents a proposal for a new slow extraction scheme for the Low Energy Ion Ring (LEIR) in the context of the feasibility study for a biomedical research facility at CERN. LEIR has to be maintained as a heavy ion accumulator ring for LHC and for fixed-target experiments with the SPS. In parallel to this on-going operation for physics experiments, an additional secondary use of LEIR for a biomedical research facility was proposed [Dosanjh2013, Holzscheiter2012, PHE2010]. This facility would complement the existing research beam-time available at other laboratories for studies related to ion beam therapy. The new slow extraction [Abler2013] is based on the third-integer resonance. The reference beam is composed of fully stripped carbon ions with extraction energies of 20-440 MeV/u, transverse physical emittances of 5-25 µm and momentum spreads of ±2-9•10-4. Two resonance driving mechanisms have been studied: the quadrupole-driven method and the RF-knockout technique. Both were made compatible...

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

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Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

Semantic reasoning with XML-based biomedical information models.

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O'Connor, Martin J; Das, Amar

2010-01-01

The Extensible Markup Language (XML) is increasingly being used for biomedical data exchange. The parallel growth in the use of ontologies in biomedicine presents opportunities for combining the two technologies to leverage the semantic reasoning services provided by ontology-based tools. There are currently no standardized approaches for taking XML-encoded biomedical information models and representing and reasoning with them using ontologies. To address this shortcoming, we have developed a workflow and a suite of tools for transforming XML-based information models into domain ontologies encoded using OWL. In this study, we applied semantics reasoning methods to these ontologies to automatically generate domain-level inferences. We successfully used these methods to develop semantic reasoning methods for information models in the HIV and radiological image domains.

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

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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

Citizen Science for Mining the Biomedical Literature

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Ginger Tsueng

2016-12-01

Full Text Available Biomedical literature represents one of the largest and fastest growing collections of unstructured biomedical knowledge. Finding critical information buried in the literature can be challenging. To extract information from free-flowing text, researchers need to: 1. identify the entities in the text (named entity recognition, 2. apply a standardized vocabulary to these entities (normalization, and 3. identify how entities in the text are related to one another (relationship extraction. Researchers have primarily approached these information extraction tasks through manual expert curation and computational methods. We have previously demonstrated that named entity recognition (NER tasks can be crowdsourced to a group of non-experts via the paid microtask platform, Amazon Mechanical Turk (AMT, and can dramatically reduce the cost and increase the throughput of biocuration efforts. However, given the size of the biomedical literature, even information extraction via paid microtask platforms is not scalable. With our web-based application Mark2Cure (http://mark2cure.org, we demonstrate that NER tasks also can be performed by volunteer citizen scientists with high accuracy. We apply metrics from the Zooniverse Matrices of Citizen Science Success and provide the results here to serve as a basis of comparison for other citizen science projects. Further, we discuss design considerations, issues, and the application of analytics for successfully moving a crowdsourcing workflow from a paid microtask platform to a citizen science platform. To our knowledge, this study is the first application of citizen science to a natural language processing task.

Advances in biomedical signal and image processing – A systematic review

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J. Rajeswari

Full Text Available Biomedical signal and image processing establish a dynamic area of specialization in both academic as well as research aspects of biomedical engineering. The concepts of signal and image processing have been widely used for extracting the physiological information in implementing many clinical procedures for sophisticated medical practices and applications. In this paper, the relationship between electrophysiological signals, i.e., electrocardiogram (ECG, electromyogram (EMG, electroencephalogram (EEG and functional image processing and their derived interactions have been discussed. Examples have been investigated in various case studies such as neurosciences, functional imaging, and cardiovascular system, by using different algorithms and methods. The interaction between the extracted information obtained from multiple signals and modalities seems to be very promising. The advanced algorithms and methods in the area of information retrieval based on time-frequency representation have been investigated. Finally, some examples of algorithms have been discussed in which the electrophysiological signals and functional images have been properly extracted and have a significant impact on various biomedical applications. Keywords: Biomedical signals and images, Processing, Analysis

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

Biomedical information retrieval across languages.

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Daumke, Philipp; Markü, Kornél; Poprat, Michael; Schulz, Stefan; Klar, Rüdiger

2007-06-01

This work presents a new dictionary-based approach to biomedical cross-language information retrieval (CLIR) that addresses many of the general and domain-specific challenges in current CLIR research. Our method is based on a multilingual lexicon that was generated partly manually and partly automatically, and currently covers six European languages. It contains morphologically meaningful word fragments, termed subwords. Using subwords instead of entire words significantly reduces the number of lexical entries necessary to sufficiently cover a specific language and domain. Mediation between queries and documents is based on these subwords as well as on lists of word-n-grams that are generated from large monolingual corpora and constitute possible translation units. The translations are then sent to a standard Internet search engine. This process makes our approach an effective tool for searching the biomedical content of the World Wide Web in different languages. We evaluate this approach using the OHSUMED corpus, a large medical document collection, within a cross-language retrieval setting.

Proposal for a new LEIR slow extraction scheme dedicated to biomedical research

CERN Document Server

Garonna, A; Abler, D

2014-01-01

A proposal is here presented for a new slow extraction scheme for the Low Energy Ion Ring (LEIR) in the context of the feasibility study for a future biomedical research facility at CERN. The new slow extraction system is based on the third-integer resonance. Two resonance driving mechanisms have been studied: the quadrupole-driven method and the RF-knockout technique. Both were made compatible with the tight constraints imposed by parallel operation of LEIR as heavy ion accumulator and care was taken to maximize the use of the available hardware.

Mining biomarker information in biomedical literature

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Younesi Erfan

2012-12-01

Full Text Available Abstract Background For selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives. Methods A biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases. Results The current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http

Finding biomedical categories in Medline®

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Yeganova Lana

2012-10-01

Full Text Available Abstract Background There are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly defined ontologies. Automatically identifying meaningful categories of entities in a large text corpus is useful for information extraction, construction of machine learning features, and development of semantic representations. In this paper we describe and compare two methods for automatically learning meaningful biomedical categories in Medline. The first approach is a simple statistical method that uses part-of-speech and frequency information to extract a list of frequent nouns from Medline. The second method implements an alignment-based technique to learn frequent generic patterns that indicate a hyponymy/hypernymy relationship between a pair of noun phrases. We then apply these patterns to Medline to collect frequent hypernyms as potential biomedical categories. Results We study and compare these two alternative sets of terms to identify semantic categories in Medline. We find that both approaches produce reasonable terms as potential categories. We also find that there is a significant agreement between the two sets of terms. The overlap between the two methods improves our confidence regarding categories predicted by these independent methods. Conclusions This study is an initial attempt to extract categories that are discussed in Medline. Rather than imposing external ontologies on Medline, our methods allow categories to emerge from the text.

[The system of biomedical scientific information of Serbia].

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Dacić, M

1995-09-01

Building of the System of biomedical scientific information of Yugoslavia (SBMSI YU) began, by the end of 1980, and the system became operative officially in 1986. After the political disintegration of former Yugoslavia SBMSI of Serbia was formed. SBMSI is developed according to the policy of developing of the System of scientific technologic information of Serbia (SSTI S), and with technical support of SSTI S. Reconstruction of the System is done by using former SBMSI YU as a model. Unlike the former SBMSI YU, SBMSI S owns besides the database Biomedicina Serbica, three important databases: database of doctoral dissertations promoted at University Medical School in Belgrade in the period from 1955-1993, database of Master's theses promoted at the University School of Medicine in Belgrade from 1965-1993; A database of foreign biomedical periodicals in libraries of Serbia.

BioCause: Annotating and analysing causality in the biomedical domain.

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Mihăilă, Claudiu; Ohta, Tomoko; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-16

Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining. We have defined an annotation scheme for enriching biomedical domain corpora with causality relations. This schema has subsequently been used to annotate 851 causal relations to form BioCause, a collection of 19 open-access full-text biomedical journal articles belonging to the subdomain of infectious diseases. These documents have been pre-annotated with named entity and event information in the context of previous shared tasks. We report an inter-annotator agreement rate of over 60% for triggers and of over 80% for arguments using an exact match constraint. These increase significantly using a relaxed match setting. Moreover, we analyse and describe the causality relations in BioCause from various points of view. This information can then be leveraged for the training of automatic causality detection systems. Augmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems. These will further influence the development of multiple tasks, such as enabling textual inference to detect entailments, discovering new facts and providing new

PASSIM – an open source software system for managing information in biomedical studies

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Neogi Sudeshna

2007-02-01

Full Text Available Abstract Background One of the crucial aspects of day-to-day laboratory information management is collection, storage and retrieval of information about research subjects and biomedical samples. An efficient link between sample data and experiment results is absolutely imperative for a successful outcome of a biomedical study. Currently available software solutions are largely limited to large-scale, expensive commercial Laboratory Information Management Systems (LIMS. Acquiring such LIMS indeed can bring laboratory information management to a higher level, but often implies sufficient investment of time, effort and funds, which are not always available. There is a clear need for lightweight open source systems for patient and sample information management. Results We present a web-based tool for submission, management and retrieval of sample and research subject data. The system secures confidentiality by separating anonymized sample information from individuals' records. It is simple and generic, and can be customised for various biomedical studies. Information can be both entered and accessed using the same web interface. User groups and their privileges can be defined. The system is open-source and is supplied with an on-line tutorial and necessary documentation. It has proven to be successful in a large international collaborative project. Conclusion The presented system closes the gap between the need and the availability of lightweight software solutions for managing information in biomedical studies involving human research subjects.

Text mining patents for biomedical knowledge.

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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.

On importance of impurities, potential leachables and extractables in algal nanocellulose for biomedical use.

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Liu, Jun; Willför, Stefan; Mihranyan, Albert

2017-09-15

Nanocellulose-based biomaterials for biomedical and pharmaceutical applications have been extensively explored. However, studies on different levels of impurities in the nanocellulose and their potential risks are lacking. This article is the most comprehensive to date survey of the importance and characterization of possible leachables and extractables in nanocellulose for biomedical use. In particular, the (1,3)-β-d-glucan interference in endotoxin detection in algal nanocellulose was addressed. Potential lipophilic and hydrophilic leachables, toxic heavy metals, and microbial contaminants are also monitored. As a model system, nanocellulose from Cladophora sp. algae is investigated. The leachable (1,3)-β-d-glucan and endotoxin, which possess strong immunogenic potential, from the cellulose were minimized to clinically insignificant levels of 4.7μg/g and 2.5EU/g, respectively. The levels of various impurities in the Cladophora cellulose are acceptable for future biomedical applications. The presented approach could be considered as a guideline for other types of nanocellulose. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Biomedical information on the internet using search engines. A one-year trial].

Science.gov (United States)

Corrao, Salvatore; Leone, Francesco; Arnone, Sabrina

2004-01-01

The internet is a communication medium and content distributor that provide information in the general sense but it could be of great utility regarding as the search and retrieval of biomedical information. Search engines represent a great deal to rapidly find information on the net. However, we do not know whether general search engines and meta-search ones are reliable in order to find useful and validated biomedical information. The aim of our study was to verify the reproducibility of a search by key-words (pediatric or evidence) using 9 international search engines and 1 meta-search engine at the baseline and after a one year period. We analysed the first 20 citations as output of each searching. We evaluated the formal quality of Web-sites and their domain extensions. Moreover, we compared the output of each search at the start of this study and after a one year period and we considered as a criterion of reliability the number of Web-sites cited again. We found some interesting results that are reported throughout the text. Our findings point out an extreme dynamicity of the information on the Web and, for this reason, we advice a great caution when someone want to use search and meta-search engines as a tool for searching and retrieve reliable biomedical information. On the other hand, some search and meta-search engines could be very useful as a first step searching for defining better a search and, moreover, for finding institutional Web-sites too. This paper allows to know a more conscious approach to the internet biomedical information universe.

BEST: Next-Generation Biomedical Entity Search Tool for Knowledge Discovery from Biomedical Literature.

Directory of Open Access Journals (Sweden)

Sunwon Lee

Full Text Available As the volume of publications rapidly increases, searching for relevant information from the literature becomes more challenging. To complement standard search engines such as PubMed, it is desirable to have an advanced search tool that directly returns relevant biomedical entities such as targets, drugs, and mutations rather than a long list of articles. Some existing tools submit a query to PubMed and process retrieved abstracts to extract information at query time, resulting in a slow response time and limited coverage of only a fraction of the PubMed corpus. Other tools preprocess the PubMed corpus to speed up the response time; however, they are not constantly updated, and thus produce outdated results. Further, most existing tools cannot process sophisticated queries such as searches for mutations that co-occur with query terms in the literature. To address these problems, we introduce BEST, a biomedical entity search tool. BEST returns, as a result, a list of 10 different types of biomedical entities including genes, diseases, drugs, targets, transcription factors, miRNAs, and mutations that are relevant to a user's query. To the best of our knowledge, BEST is the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results. BEST is freely accessible at http://best.korea.ac.kr.

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

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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

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

Information extraction system

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Lemmond, Tracy D; Hanley, William G; Guensche, Joseph Wendell; Perry, Nathan C; Nitao, John J; Kidwell, Paul Brandon; Boakye, Kofi Agyeman; Glaser, Ron E; Prenger, Ryan James

2014-05-13

An information extraction system and methods of operating the system are provided. In particular, an information extraction system for performing meta-extraction of named entities of people, organizations, and locations as well as relationships and events from text documents are described herein.

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

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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.

[Biomedical informatics].

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Capurro, Daniel; Soto, Mauricio; Vivent, Macarena; Lopetegui, Marcelo; Herskovic, Jorge R

2011-12-01

Biomedical Informatics is a new discipline that arose from the need to incorporate information technologies to the generation, storage, distribution and analysis of information in the domain of biomedical sciences. This discipline comprises basic biomedical informatics, and public health informatics. The development of the discipline in Chile has been modest and most projects have originated from the interest of individual people or institutions, without a systematic and coordinated national development. Considering the unique features of health care system of our country, research in the area of biomedical informatics is becoming an imperative.

Institute for Scientific Information-indexed biomedical journals of Saudi Arabia

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Rohra, Dileep K.; Rohra, Vikram K.; Cahusac, Peter

2016-01-01

Objectives: To compare the journal impact factor (JIF) and Eigenfactor score (ES) of Institute for Scientific Information (ISI)-indexed biomedical journals published from the Kingdom of Saudi Arabia (KSA) over the last 8 years. Methods: This is a retrospective study, conducted at Alfaisal University, Riyadh, KSA from January to March 2016. The Journal Citation Reports of ISI Web of Knowledge were accessed, and 6 Saudi biomedical journals were included for analysis. Results: All Saudi journals have improved their IF compared with their baseline. However, the performance of the Journal of Pharmaceutical Sciences and Neurosciences has been exceptionally good. The biggest improvement in percent growth in JIF was seen in the Saudi Pharmaceutical Journal (approximately 887%) followed by Neurosciences (approximately 462%). Interestingly, the ES of all biomedical journals, except Saudi Journal of Gastroenterology and Saudi Medical Journal, increased over the years. The greatest growth in ES (more than 5 fold) was noted for Neurosciences and Saudi Pharmaceutical Journal. Conclusion: This study shows that the overall quality of all Saudi biomedical journals has improved in the last 8 years. PMID:27761565

Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension.

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Yu, Hong; Agarwal, Shashank; Johnston, Mark; Cohen, Aaron

2009-01-06

Biomedical scientists need to access figures to validate research facts and to formulate or to test novel research hypotheses. However, figures are difficult to comprehend without associated text (e.g., figure legend and other reference text). We are developing automated systems to extract the relevant explanatory information along with figures extracted from full text articles. Such systems could be very useful in improving figure retrieval and in reducing the workload of biomedical scientists, who otherwise have to retrieve and read the entire full-text journal article to determine which figures are relevant to their research. As a crucial step, we studied the importance of associated text in biomedical figure comprehension. Twenty subjects evaluated three figure-text combinations: figure+legend, figure+legend+title+abstract, and figure+full-text. Using a Likert scale, each subject scored each figure+text according to the extent to which the subject thought he/she understood the meaning of the figure and the confidence in providing the assigned score. Additionally, each subject entered a free text summary for each figure-text. We identified missing information using indicator words present within the text summaries. Both the Likert scores and the missing information were statistically analyzed for differences among the figure-text types. We also evaluated the quality of text summaries with the text-summarization evaluation method the ROUGE score. Our results showed statistically significant differences in figure comprehension when varying levels of text were provided. When the full-text article is not available, presenting just the figure+legend left biomedical researchers lacking 39-68% of the information about a figure as compared to having complete figure comprehension; adding the title and abstract improved the situation, but still left biomedical researchers missing 30% of the information. When the full-text article is available, figure comprehension

Organization of Biomedical Data for Collaborative Scientific Research: A Research Information Management System.

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Myneni, Sahiti; Patel, Vimla L

2010-06-01

Biomedical researchers often work with massive, detailed and heterogeneous datasets. These datasets raise new challenges of information organization and management for scientific interpretation, as they demand much of the researchers' time and attention. The current study investigated the nature of the problems that researchers face when dealing with such data. Four major problems identified with existing biomedical scientific information management methods were related to data organization, data sharing, collaboration, and publications. Therefore, there is a compelling need to develop an efficient and user-friendly information management system to handle the biomedical research data. This study evaluated the implementation of an information management system, which was introduced as part of the collaborative research to increase scientific productivity in a research laboratory. Laboratory members seemed to exhibit frustration during the implementation process. However, empirical findings revealed that they gained new knowledge and completed specified tasks while working together with the new system. Hence, researchers are urged to persist and persevere when dealing with any new technology, including an information management system in a research laboratory environment.

Minimally-invasive, microneedle-array extraction of interstitial fluid for comprehensive biomedical applications: transcriptomics, proteomics, metabolomics, exosome research, and biomarker identification.

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Taylor, Robert M; Miller, Philip R; Ebrahimi, Parwana; Polsky, Ronen; Baca, Justin T

2018-01-01

Interstitial fluid (ISF) has recently garnered interest as a biological fluid that could be used as an alternate to blood for biomedical applications, diagnosis, and therapy. ISF extraction techniques are promising because they are less invasive and less painful than venipuncture. ISF is an alternative, incompletely characterized source of physiological data. Here, we describe a novel method of ISF extraction in rats, using microneedle arrays, which provides volumes of ISF that are sufficient for downstream analysis techniques such as proteomics, genomics, and extracellular vesicle purification and analysis. This method is potentially less invasive than previously reported techniques. The limited invasiveness and larger volumes of extracted ISF afforded by this microneedle-assisted ISF extraction method provide a technique that is less stressful and more humane to laboratory animals, while also allowing for a reduction in the numbers of animals needed to acquire sufficient volumes of ISF for biomedical analysis and application.

Portable blood extraction device integrated with biomedical monitoring system

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Khumpuang, S.; Horade, M.; Fujioka, K.; Sugiyama, S.

2006-01-01

Painless and portable blood extraction device has been immersed in the world of miniaturization on bio-medical research particularly in manufacturing point-of-care systems. The fabrication of a blood extraction device integrated with an electrolyte-monitoring system is reported in this paper. The device has advantages in precise controlled dosage of blood extracted including the slightly damaged blood vessels and nervous system. The in-house blood diagnostic will become simple for the patients. Main components of the portable system are; the blood extraction device and electrolyte-monitoring system. The monitoring system consists of ISFET (Ion Selective Field Effect Transistor) for measuring the concentration level of minerals in blood. In this work, we measured the level of 3 ions; Na+, K+ and Cl-. The mentioned ions are frequently required the measurement since their concentration levels in the blood can indicate whether the kidney, pancreas, liver or heart is being malfunction. The fabrication of the whole system and experimentation on each ISM (Ion Sensitive Membrane) will be provided. Taking the advantages of LIGA technology, the 100 hollow microneedles fabricated by Synchrotron Radiation deep X-ray lithography through PCT (Plane-pattern to Cross-section Transfer) technique have been consisted in 5x5 mm2 area. The microneedle is 300 μm in base-diameter, 500 μm-pitch, 800 μm-height and 50 μm hole-diameter. The total size of the blood extraction device is 2x2x2 cm 3. The package is made from a plastic socket including slots for inserting microneedle array and ISFET connecting to an electrical circuit for the monitoring. Through the dimensional design for simply handling and selection of disposable material, the patients can self-evaluate the critical level of the body minerals in anywhere and anytime.

In-line phase contrast micro-CT reconstruction for biomedical specimens.

Science.gov (United States)

Fu, Jian; Tan, Renbo

2014-01-01

X-ray phase contrast micro computed tomography (micro-CT) can non-destructively provide the internal structure information of soft tissues and low atomic number materials. It has become an invaluable analysis tool for biomedical specimens. Here an in-line phase contrast micro-CT reconstruction technique is reported, which consists of a projection extraction method and the conventional filter back-projection (FBP) reconstruction algorithm. The projection extraction is implemented by applying the Fourier transform to the forward projections of in-line phase contrast micro-CT. This work comprises a numerical study of the method and its experimental verification using a biomedical specimen dataset measured at an X-ray tube source micro-CT setup. The numerical and experimental results demonstrate that the presented technique can improve the imaging contrast of biomedical specimens. It will be of interest for a wide range of in-line phase contrast micro-CT applications in medicine and biology.

MOLIERE: Automatic Biomedical Hypothesis Generation System.

Science.gov (United States)

Sybrandt, Justin; Shtutman, Michael; Safro, Ilya

2017-08-01

Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

Science.gov (United States)

Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas

2007-02-01

The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Investigating and Annotating the Role of Citation in Biomedical Full-Text Articles.

Science.gov (United States)

Yu, Hong; Agarwal, Shashank; Frid, Nadya

2009-11-01

Citations are ubiquitous in scientific articles and play important roles for representing the semantic content of a full-text biomedical article. In this work, we manually examined full-text biomedical articles to analyze the semantic content of citations in full-text biomedical articles. After developing a citation relation schema and annotation guideline, our pilot annotation results show an overall agreement of 0.71, and here we report on the research challenges and the lessons we've learned while trying to overcome them. Our work is a first step toward automatic citation classification in full-text biomedical articles, which may contribute to many text mining tasks, including information retrieval, extraction, summarization, and question answering.

Extracting microRNA-gene relations from biomedical literature using distant supervision.

Directory of Open Access Journals (Sweden)

Andre Lamurias

Full Text Available Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.

Multimedia Information Extraction

CERN Document Server

Maybury, Mark T

2012-01-01

The advent of increasingly large consumer collections of audio (e.g., iTunes), imagery (e.g., Flickr), and video (e.g., YouTube) is driving a need not only for multimedia retrieval but also information extraction from and across media. Furthermore, industrial and government collections fuel requirements for stock media access, media preservation, broadcast news retrieval, identity management, and video surveillance.  While significant advances have been made in language processing for information extraction from unstructured multilingual text and extraction of objects from imagery and vid

A System for Information Management in BioMedical Studies—SIMBioMS

Science.gov (United States)

Krestyaninova, Maria; Zarins, Andris; Viksna, Juris; Kurbatova, Natalja; Rucevskis, Peteris; Neogi, Sudeshna Guha; Gostev, Mike; Perheentupa, Teemu; Knuuttila, Juha; Barrett, Amy; Lappalainen, Ilkka; Rung, Johan; Podnieks, Karlis; Sarkans, Ugis; McCarthy, Mark I; Brazma, Alvis

2009-01-01

Summary: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented. Availability: The source code, documentation and initialization scripts are available at http://simbioms.org. Contact: support@simbioms.org; mariak@ebi.ac.uk PMID:19633095

Strategies for Disseminating Information on Biomedical Research on Autism to Hispanic Parents

Science.gov (United States)

Lajonchere, Clara M.; Wheeler, Barbara Y.; Valente, Thomas W.; Kreutzer, Cary; Munson, Aron; Narayanan, Shrikanth; Kazemzadeh, Abe; Cruz, Roxana; Martinez, Irene; Schrager, Sheree M.; Schweitzer, Lisa; Chklovski, Tara; Hwang, Darryl

2016-01-01

Low income Hispanic families experience multiple barriers to accessing evidence-based information on Autism Spectrum Disorders (ASD). This study utilized a mixed-strategy intervention to create access to information in published bio-medical research articles on ASD by distilling the content into parent-friendly English- and Spanish-language ASD…

Modeling and mining term association for improving biomedical information retrieval performance.

Science.gov (United States)

Hu, Qinmin; Huang, Jimmy Xiangji; Hu, Xiaohua

2012-06-11

The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent

Method for detecting core malware sites related to biomedical information systems.

Science.gov (United States)

Kim, Dohoon; Choi, Donghee; Jin, Jonghyun

2015-01-01

Most advanced persistent threat attacks target web users through malicious code within landing (exploit) or distribution sites. There is an urgent need to block the affected websites. Attacks on biomedical information systems are no exception to this issue. In this paper, we present a method for locating malicious websites that attempt to attack biomedical information systems. Our approach uses malicious code crawling to rearrange websites in the order of their risk index by analyzing the centrality between malware sites and proactively eliminates the root of these sites by finding the core-hub node, thereby reducing unnecessary security policies. In particular, we dynamically estimate the risk index of the affected websites by analyzing various centrality measures and converting them into a single quantified vector. On average, the proactive elimination of core malicious websites results in an average improvement in zero-day attack detection of more than 20%.

Method for Detecting Core Malware Sites Related to Biomedical Information Systems

Directory of Open Access Journals (Sweden)

Dohoon Kim

2015-01-01

Full Text Available Most advanced persistent threat attacks target web users through malicious code within landing (exploit or distribution sites. There is an urgent need to block the affected websites. Attacks on biomedical information systems are no exception to this issue. In this paper, we present a method for locating malicious websites that attempt to attack biomedical information systems. Our approach uses malicious code crawling to rearrange websites in the order of their risk index by analyzing the centrality between malware sites and proactively eliminates the root of these sites by finding the core-hub node, thereby reducing unnecessary security policies. In particular, we dynamically estimate the risk index of the affected websites by analyzing various centrality measures and converting them into a single quantified vector. On average, the proactive elimination of core malicious websites results in an average improvement in zero-day attack detection of more than 20%.

Challenges in Managing Information Extraction

Science.gov (United States)

Shen, Warren H.

2009-01-01

This dissertation studies information extraction (IE), the problem of extracting structured information from unstructured data. Example IE tasks include extracting person names from news articles, product information from e-commerce Web pages, street addresses from emails, and names of emerging music bands from blogs. IE is all increasingly…

Magnetic nanoparticles for biomedical applications

International Nuclear Information System (INIS)

Krustev, P.; Ruskov, T.

2007-01-01

In this paper we describe different biomedical application using magnetic nanoparticles. Over the past decade, a number of biomedical applications have begun to emerge for magnetic nanoparticles of differing sizes, shapes, and compositions. Areas under investigation include targeted drug delivery, ultra-sensitive disease detection, gene therapy, high throughput genetic screening, biochemical sensing, and rapid toxicity cleansing. Magnetic nanoparticles exhibit ferromagnetic or superparamagnetic behavior, magnetizing strongly under an applied field. In the second case (superparamagnetic nanoparticles) there is no permanent magnetism once the field is removed. The superparamagnetic nanoparticles are highly attractive as in vivo probes or in vitro tools to extract information on biochemical systems. The optical properties of magnetic metal nanoparticles are spectacular and, therefore, have promoted a great deal of excitement during the last few decades. Many applications as MRI imaging and hyperthermia rely on the use of iron oxide particles. Moreover magnetic nanoparticles conjugated with antibodies are also applied to hyperthermia and have enabled tumor specific contrast enhancement in MRI. Other promising biomedical applications are connected with tumor cells treated with magnetic nanoparticles with X-ray ionizing radiation, which employs magnetic nanoparticles as a complementary radiate source inside the tumor. (authors)

A Study of the Information Literacy of Biomedical Graduate Students: Based on the Thesis Topic Discovery Process in Molecular Biology Research

Directory of Open Access Journals (Sweden)

Jhao-Yen Huang

2014-06-01

Full Text Available The biomedical information environment is in a state of constant and rapid change due to the increase in research data and rapid technological advances. In Taiwan, few research has investigated the information literacy of biomedical graduate students. This exploratory study examined the information literacy abilities and training of biomedical graduate students in Taiwan. Semi-structured interviews based on the Association of College and Research Libraries Information Literacy Competency Standards for Science and Engineering/Technology were conducted with 20 molecular biological graduate students. The interview inquired about their information-seeking channels and information literacy education. The findings show that the biomedical graduate students developed a workable thesis topic with their advisors. Through various information-seeking channels and retrieval strategies, they obtained and critically evaluated information to address different information needs for their thesis research. Through seminars, annual conferences and papers, the interviewees were informed of current developments in their field. Subsequently, through written or oral communications, they were able to integrate and exchange the information. Most interviewees cared about the social, economic, legal, and ethical issues surrounding the use of information. College courses and labs were the main information literacy education environment for them to learn about research skills and knowledge. The study concludes four areas to address for the information literacy of biomedical graduate students, i.e., using professional information, using the current information, efficiency in assessing the domain information, and utilization of diverse information channels. Currently, the interviewees showed rather low usage of library resources, which is a concern for biomedical educators and libraries. [Article content in Chinese

Developing a search engine for pharmacotherapeutic information that is not published in biomedical journals.

Science.gov (United States)

Do Pazo-Oubiña, F; Calvo Pita, C; Puigventós Latorre, F; Periañez-Párraga, L; Ventayol Bosch, P

2011-01-01

To identify publishers of pharmacotherapeutic information not found in biomedical journals that focuses on evaluating and providing advice on medicines and to develop a search engine to access this information. Compiling web sites that publish information on the rational use of medicines and have no commercial interests. Free-access web sites in Spanish, Galician, Catalan or English. Designing a search engine using the Google "custom search" application. Overall 159 internet addresses were compiled and were classified into 9 labels. We were able to recover the information from the selected sources using a search engine, which is called "AlquimiA" and available from http://www.elcomprimido.com/FARHSD/AlquimiA.htm. The main sources of pharmacotherapeutic information not published in biomedical journals were identified. The search engine is a useful tool for searching and accessing "grey literature" on the internet. Copyright © 2010 SEFH. Published by Elsevier Espana. All rights reserved.

Semantics-driven modelling of user preferences for information retrieval in the biomedical domain.

Science.gov (United States)

Gladun, Anatoly; Rogushina, Julia; Valencia-García, Rafael; Béjar, Rodrigo Martínez

2013-03-01

A large amount of biomedical and genomic data are currently available on the Internet. However, data are distributed into heterogeneous biological information sources, with little or even no organization. Semantic technologies provide a consistent and reliable basis with which to confront the challenges involved in the organization, manipulation and visualization of data and knowledge. One of the knowledge representation techniques used in semantic processing is the ontology, which is commonly defined as a formal and explicit specification of a shared conceptualization of a domain of interest. The work presented here introduces a set of interoperable algorithms that can use domain and ontological information to improve information-retrieval processes. This work presents an ontology-based information-retrieval system for the biomedical domain. This system, with which some experiments have been carried out that are described in this paper, is based on the use of domain ontologies for the creation and normalization of lightweight ontologies that represent user preferences in a determined domain in order to improve information-retrieval processes.

Use of systematic review to inform the infection risk for biomedical engineers and technicians servicing biomedical devices

International Nuclear Information System (INIS)

Smith, Anne-Louise

2011-01-01

Full text: Many microorganisms responsible for hospital acquired infections are able to stay viable on surfaces with no visible sign of contamination, in dry conditions and on non-porous surfaces. The infection risk to biomedical staff when servicing biomedical devices is not documented. An indirect approach has been used to examine the different aspects that will affect the risk of infection including a systematic review of microbial contamination and transmission relating to biomedical devices. A systematic review found 58% of biomedical devices have microbial contamination with 13% having at least one pathogenic organism. These microbes can persist for some months. Occupational-infections of biomedical service staff are low compared to other healthcare workers. A biomedical device with contaminated surface or dust was identified as the source of patient outbreaks in 13 papers. The cleaning agent most tested for removal of micro-organisms from devices was alcohol swabs, but sterile water swabs were also effective. However, manufacturers mainly recommend (74%) cleaning devices with water and detergent. Biomedical engineers and technicians have a small risk of being exposed to dangerous micro-organisms on most biomedical devices, but without skin breakage, this exposure is unlikely to cause ill-health. It is recommended that biomedical staff follow good infection control practices, wipe devices with detergent, sterile water or alcohol swabs as recommended by the manufacturer before working on them, and keep alcohol hand rubs accessible at all benches. (author)

Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications.

Science.gov (United States)

Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart

2013-01-01

Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

CNN-based ranking for biomedical entity normalization.

Science.gov (United States)

Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Huang, Dong

2017-10-03

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

Generic information can retrieve known biological associations: implications for biomedical knowledge discovery.

Directory of Open Access Journals (Sweden)

Herman H H B M van Haagen

Full Text Available MOTIVATION: Weighted semantic networks built from text-mined literature can be used to retrieve known protein-protein or gene-disease associations, and have been shown to anticipate associations years before they are explicitly stated in the literature. Our text-mining system recognizes over 640,000 biomedical concepts: some are specific (i.e., names of genes or proteins others generic (e.g., 'Homo sapiens'. Generic concepts may play important roles in automated information retrieval, extraction, and inference but may also result in concept overload and confound retrieval and reasoning with low-relevance or even spurious links. Here, we attempted to optimize the retrieval performance for protein-protein interactions (PPI by filtering generic concepts (node filtering or links to generic concepts (edge filtering from a weighted semantic network. First, we defined metrics based on network properties that quantify the specificity of concepts. Then using these metrics, we systematically filtered generic information from the network while monitoring retrieval performance of known protein-protein interactions. We also systematically filtered specific information from the network (inverse filtering, and assessed the retrieval performance of networks composed of generic information alone. RESULTS: Filtering generic or specific information induced a two-phase response in retrieval performance: initially the effects of filtering were minimal but beyond a critical threshold network performance suddenly drops. Contrary to expectations, networks composed exclusively of generic information demonstrated retrieval performance comparable to unfiltered networks that also contain specific concepts. Furthermore, an analysis using individual generic concepts demonstrated that they can effectively support the retrieval of known protein-protein interactions. For instance the concept "binding" is indicative for PPI retrieval and the concept "mutation abnormality" is

Implementation and management of a biomedical observation dictionary in a large healthcare information system.

Science.gov (United States)

Vandenbussche, Pierre-Yves; Cormont, Sylvie; André, Christophe; Daniel, Christel; Delahousse, Jean; Charlet, Jean; Lepage, Eric

2013-01-01

This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system. AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary. This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC. This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.

The Influence of Biomedical Information and Childhood History on Sentencing.

Science.gov (United States)

Kim, JongHan; Boytos, Abby; Seong, Yoori; Park, Kwangbai

2015-01-01

A recent trend in court is for defense attorneys to introduce brain scans and other forms of biomedical information (BI) into criminal trials as mitigating evidence. The present study investigates how BI, when considered in combination with a defendant's childhood information (CI), can influence the length of a defendant's sentence. We hypothesized that certain combinations of BI and CI result in shorter sentences because they suggest that the defendant poses less of a threat to society. Participants were asked to read accounts of the trial of a murder suspect and, based on the information therein, recommend a sentence as if they were the judge. The defendant was diagnosed with psychopathy, but biomedical information regarding that diagnosis was included or excluded depending on the BI condition. The defendant was further described as growing up in either a loving or abusive family. The results showed that, if BI was present in the trial account, the defendant from an abusive family was perceived as less of a threat to society and received a shorter recommended sentence than if the defendant had been from a loving family. If BI was absent from the account, the pattern was reversed: the defendant from a loving family was perceived as less of a threat to society and received a shorter recommended sentence than if he had been from an abusive family. Implications for the use of BI and CI in court trials are discussed, as well as their relationship to free will and the function of punishment as retribution and utility. Copyright © 2015 John Wiley & Sons, Ltd.

Electromembrane extraction for pharmaceutical and biomedical analysis

DEFF Research Database (Denmark)

Huang, Chuixiu; Seip, Knut Fredrik; Gjelstad, Astrid

2015-01-01

. The present paper discusses recent development of EME. The paper focuses on the principles of EME, and discusses how to optimize operational parameters. In addition, pharmaceutical and biomedical applications of EME are reviewed, with emphasis on basic drugs, acidic drugs, amino acids, and peptides. Finally...

Scholarly Information Extraction Is Going to Make a Quantum Leap with PubMed Central (PMC).

Science.gov (United States)

Matthies, Franz; Hahn, Udo

2017-01-01

With the increasing availability of complete full texts (journal articles), rather than their surrogates (titles, abstracts), as resources for text analytics, entirely new opportunities arise for information extraction and text mining from scholarly publications. Yet, we gathered evidence that a range of problems are encountered for full-text processing when biomedical text analytics simply reuse existing NLP pipelines which were developed on the basis of abstracts (rather than full texts). We conducted experiments with four different relation extraction engines all of which were top performers in previous BioNLP Event Extraction Challenges. We found that abstract-trained engines loose up to 6.6% F-score points when run on full-text data. Hence, the reuse of existing abstract-based NLP software in a full-text scenario is considered harmful because of heavy performance losses. Given the current lack of annotated full-text resources to train on, our study quantifies the price paid for this short cut.

Environmental/Biomedical Terminology Index

International Nuclear Information System (INIS)

Huffstetler, J.K.; Dailey, N.S.; Rickert, L.W.; Chilton, B.D.

1976-12-01

The Information Center Complex (ICC), a centrally administered group of information centers, provides information support to environmental and biomedical research groups and others within and outside Oak Ridge National Laboratory. In-house data base building and development of specialized document collections are important elements of the ongoing activities of these centers. ICC groups must be concerned with language which will adequately classify and insure retrievability of document records. Language control problems are compounded when the complexity of modern scientific problem solving demands an interdisciplinary approach. Although there are several word lists, indexes, and thesauri specific to various scientific disciplines usually grouped as Environmental Sciences, no single generally recognized authority can be used as a guide to the terminology of all environmental science. If biomedical terminology for the description of research on environmental effects is also needed, the problem becomes even more complex. The building of a word list which can be used as a general guide to the environmental/biomedical sciences has been a continuing activity of the Information Center Complex. This activity resulted in the publication of the Environmental Biomedical Terminology Index

Therapeutic Significance of Loligo vulgaris (Lamarck, 1798) ink Extract: A Biomedical Approach

Science.gov (United States)

Nadarajah, Sri Kumaran; Vijayaraj, Radha; Mani, Jayaprakashvel

2017-01-01

Background: The squid ink extract is well known for its biomedical properties. Objective: In this study, squid Loligo vulgaris was collected from Tuticorin costal water, Bay of Bengal, India. Materials and Methods: Proximate composition of the crude squid ink was studied and found to have protein as the major component over lipid and carbohydrates. Further, bioactive fractions of squid ink were extracted with ethanol, and therapeutic applications such as hemolytic, antioxidant, antimicrobial, and in vitro anti-inflammatory properties were analyzed using standard methods. Results: In hemolytic assay, the squid ink extract exhibited a maximum hemolytic activity of 128 hemolytic unit against tested erythrocytes. In DPPH assay, the ethanolic extract of squid ink has exhibited an antioxidant activity of 83.5%. The squid ink was found to be potent antibacterial agent against the pathogens tested. 200 μL of L. vulgaris ink extract showed remarkable antibacterial activity as zone of inhibition against Escherichia coli (28 mm), Klebsiella pneumoniae (22 mm), Pseudomonas aeruginosa (21 mm), and Staphylococcus aureus (24 mm). The 68.9% inhibition of protein denaturation by the squid ink extract indicated that it has very good in vitro anti-inflammatory properties. The Fourier transform infrared spectroscopy analysis of the ethanolic extracts of the squid ink indicated the presence of functional groups such as 1° and 2° amines, amides, alkynes (terminal), alkenes, aldehydes, nitriles, alkanes, aliphatic amines, carboxylic acids, and alkyl halides, which complements the biochemical background of therapeutic applications. Conclusion: Hence, results of this study concluded that the ethanolic extract of L. vulgaris has many therapeutic applications such as antimicrobial, antioxidant, and anti-inflammatory activities. SUMMARY Squid ink is very high in a number of important nutrients. It’s particularly high in antioxidants for instance, which as well all know help to protect

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos.

Science.gov (United States)

Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian

2016-04-01

Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today's keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users' information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively.ResultsThe authors produced a prototype implementation of the proposed system, which is publicly accessible athttps://patentq.njit.edu/oer To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable

Environmental/Biomedical Terminology Index

Energy Technology Data Exchange (ETDEWEB)

Huffstetler, J.K.; Dailey, N.S.; Rickert, L.W.; Chilton, B.D.

1976-12-01

The Information Center Complex (ICC), a centrally administered group of information centers, provides information support to environmental and biomedical research groups and others within and outside Oak Ridge National Laboratory. In-house data base building and development of specialized document collections are important elements of the ongoing activities of these centers. ICC groups must be concerned with language which will adequately classify and insure retrievability of document records. Language control problems are compounded when the complexity of modern scientific problem solving demands an interdisciplinary approach. Although there are several word lists, indexes, and thesauri specific to various scientific disciplines usually grouped as Environmental Sciences, no single generally recognized authority can be used as a guide to the terminology of all environmental science. If biomedical terminology for the description of research on environmental effects is also needed, the problem becomes even more complex. The building of a word list which can be used as a general guide to the environmental/biomedical sciences has been a continuing activity of the Information Center Complex. This activity resulted in the publication of the Environmental Biomedical Terminology Index (EBTI).

Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec.

Science.gov (United States)

Zhu, Yongjun; Yan, Erjia; Wang, Fei

2017-07-03

Understanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec's ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec. We download abstracts of 18,777,129 articles from PubMed and 766,326 full-text articles from PubMed Central (PMC). The datasets are preprocessed and grouped into subsets by recency, size, and section. Word2vec models are trained on these subtests. Cosine similarities between biomedical terms obtained from the word2vec models are compared against reference standards. Performance of models trained on different subsets are compared to examine recency, size, and section effects. Models trained on recent datasets did not boost the performance. Models trained on larger datasets identified more pairs of biomedical terms than models trained on smaller datasets in relatedness task (from 368 at the 10% level to 494 at the 100% level) and similarity task (from 374 at the 10% level to 491 at the 100% level). The model trained on abstracts produced results that have higher correlations with the reference standards than the one trained on article bodies (i.e., 0.65 vs. 0.62 in the similarity task and 0.66 vs. 0.59 in the relatedness task). However, the latter identified more pairs of biomedical terms than the former (i.e., 344 vs. 498 in the similarity task and 339 vs. 503 in the relatedness task). Increasing the size of dataset does not always enhance the performance. Increasing the size of datasets can result in the identification of more relations of biomedical terms even though it does not guarantee better precision. As summaries of research articles, compared with article

Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

Science.gov (United States)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

Information extraction from multi-institutional radiology reports.

Science.gov (United States)

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinical and research use. Although efforts to structure some radiology report information through predefined templates are beginning to bear fruit, a large portion of radiology report information is entered in free text. The free text format is a major obstacle for rapid extraction and subsequent use of information by clinicians, researchers, and healthcare information systems. This difficulty is due to the ambiguity and subtlety of natural language, complexity of described images, and variations among different radiologists and healthcare organizations. As a result, radiology reports are used only once by the clinician who ordered the study and rarely are used again for research and data mining. In this work, machine learning techniques and a large multi-institutional radiology report repository are used to extract the semantics of the radiology report and overcome the barriers to the re-use of radiology report information in clinical research and other healthcare applications. We describe a machine learning system to annotate radiology reports and extract report contents according to an information model. This information model covers the majority of clinically significant contents in radiology reports and is applicable to a wide variety of radiology study types. Our automated approach uses discriminative sequence classifiers for named-entity recognition to extract and organize clinically significant terms and phrases consistent with the information model. We evaluated our information extraction system on 150 radiology reports from three major healthcare organizations and compared its results to a commonly used non-machine learning information extraction method. We

Analyzing rare diseases terms in biomedical terminologies

Directory of Open Access Journals (Sweden)

Erika Pasceri

2012-03-01

Full Text Available Rare disease patients too often face common problems, including the lack of access to correct diagnosis, lack of quality information on the disease, lack of scientific knowledge of the disease, inequities and difficulties in access to treatment and care. These things could be changed by implementing a comprehensive approach to rare diseases, increasing international cooperation in scientific research, by gaining and sharing scientific knowledge about and by developing tools for extracting and sharing knowledge. A significant aspect to analyze is the organization of knowledge in the biomedical field for the proper management and recovery of health information. For these purposes, the sources needed have been acquired from the Office of Rare Diseases Research, the National Organization of Rare Disorders and Orphanet, organizations that provide information to patients and physicians and facilitate the exchange of information among different actors involved in this field. The present paper shows the representation of rare diseases terms in biomedical terminologies such as MeSH, ICD-10, SNOMED CT and OMIM, leveraging the fact that these terminologies are integrated in the UMLS. At the first level, it was analyzed the overlap among sources and at a second level, the presence of rare diseases terms in target sources included in UMLS, working at the term and concept level. We found that MeSH has the best representation of rare diseases terms.

A service-oriented distributed semantic mediator: integrating multiscale biomedical information.

Science.gov (United States)

Mora, Oscar; Engelbrecht, Gerhard; Bisbal, Jesus

2012-11-01

Biomedical research continuously generates large amounts of heterogeneous and multimodal data spread over multiple data sources. These data, if appropriately shared and exploited, could dramatically improve the research practice itself, and ultimately the quality of health care delivered. This paper presents DISMED (DIstributed Semantic MEDiator), an open source semantic mediator that provides a unified view of a federated environment of multiscale biomedical data sources. DISMED is a Web-based software application to query and retrieve information distributed over a set of registered data sources, using semantic technologies. It also offers a userfriendly interface specifically designed to simplify the usage of these technologies by non-expert users. Although the architecture of the software mediator is generic and domain independent, in the context of this paper, DISMED has been evaluated for managing biomedical environments and facilitating research with respect to the handling of scientific data distributed in multiple heterogeneous data sources. As part of this contribution, a quantitative evaluation framework has been developed. It consist of a benchmarking scenario and the definition of five realistic use-cases. This framework, created entirely with public datasets, has been used to compare the performance of DISMED against other available mediators. It is also available to the scientific community in order to evaluate progress in the domain of semantic mediation, in a systematic and comparable manner. The results show an average improvement in the execution time by DISMED of 55% compared to the second best alternative in four out of the five use-cases of the experimental evaluation.

Using Local Grammar for Entity Extraction from Clinical Reports

Directory of Open Access Journals (Sweden)

Aicha Ghoulam

2015-06-01

Full Text Available Information Extraction (IE is a natural language processing (NLP task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%.

Three-dimensional biomedical imaging

International Nuclear Information System (INIS)

Robb, R.A.

1985-01-01

Scientists in biomedical imaging provide researchers, physicians, and academicians with an understanding of the fundamental theories and practical applications of three-dimensional biomedical imaging methodologies. Succinct descriptions of each imaging modality are supported by numerous diagrams and illustrations which clarify important concepts and demonstrate system performance in a variety of applications. Comparison of the different functional attributes, relative advantages and limitations, complementary capabilities, and future directions of three-dimensional biomedical imaging modalities are given. Volume 1: Introductions to Three-Dimensional Biomedical Imaging Photoelectronic-Digital Imaging for Diagnostic Radiology. X-Ray Computed Tomography - Basic Principles. X-Ray Computed Tomography - Implementation and Applications. X-Ray Computed Tomography: Advanced Systems and Applications in Biomedical Research and Diagnosis. Volume II: Single Photon Emission Computed Tomography. Position Emission Tomography (PET). Computerized Ultrasound Tomography. Fundamentals of NMR Imaging. Display of Multi-Dimensional Biomedical Image Information. Summary and Prognostications

Data integration and knowledge discovery in biomedical databases. Reliable information from unreliable sources

Directory of Open Access Journals (Sweden)

A Mitnitski

2003-01-01

Full Text Available To better understand information about human health from databases we analyzed three datasets collected for different purposes in Canada: a biomedical database of older adults, a large population survey across all adult ages, and vital statistics. Redundancy in the variables was established, and this led us to derive a generalized (macroscopic state variable, being a fitness/frailty index that reflects both individual and group health status. Evaluation of the relationship between fitness/frailty and the mortality rate revealed that the latter could be expressed in terms of variables generally available from any cross-sectional database. In practical terms, this means that the risk of mortality might readily be assessed from standard biomedical appraisals collected for other purposes.

Resource Disambiguator for the Web: Extracting Biomedical Resources and Their Citations from the Scientific Literature.

Directory of Open Access Journals (Sweden)

Ibrahim Burak Ozyurt

Full Text Available The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web. RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual

BioN∅T: A searchable database of biomedical negated sentences

Directory of Open Access Journals (Sweden)

Agarwal Shashank

2011-10-01

Full Text Available Abstract Background Negated biomedical events are often ignored by text-mining applications; however, such events carry scientific significance. We report on the development of BioN∅T, a database of negated sentences that can be used to extract such negated events. Description Currently BioN∅T incorporates ≈32 million negated sentences, extracted from over 336 million biomedical sentences from three resources: ≈2 million full-text biomedical articles in Elsevier and the PubMed Central, as well as ≈20 million abstracts in PubMed. We evaluated BioN∅T on three important genetic disorders: autism, Alzheimer's disease and Parkinson's disease, and found that BioN∅T is able to capture negated events that may be ignored by experts. Conclusions The BioN∅T database can be a useful resource for biomedical researchers. BioN∅T is freely available at http://bionot.askhermes.org/. In future work, we will develop semantic web related technologies to enrich BioN∅T.

Extracting useful information from images

DEFF Research Database (Denmark)

Kucheryavskiy, Sergey

2011-01-01

The paper presents an overview of methods for extracting useful information from digital images. It covers various approaches that utilized different properties of images, like intensity distribution, spatial frequencies content and several others. A few case studies including isotropic and heter......The paper presents an overview of methods for extracting useful information from digital images. It covers various approaches that utilized different properties of images, like intensity distribution, spatial frequencies content and several others. A few case studies including isotropic...

Visualization and classification in biomedical terahertz pulsed imaging

International Nuclear Information System (INIS)

Loeffler, Torsten; Siebert, Karsten; Czasch, Stephanie; Bauer, Tobias; Roskos, Hartmut G

2002-01-01

'Visualization' in imaging is the process of extracting useful information from raw data in such a way that meaningful physical contrasts are developed. 'Classification' is the subsequent process of defining parameter ranges which allow us to identify elements of images such as different tissues or different objects. In this paper, we explore techniques for visualization and classification in terahertz pulsed imaging (TPI) for biomedical applications. For archived (formalin-fixed, alcohol-dehydrated and paraffin-mounted) test samples, we investigate both time- and frequency-domain methods based on bright- and dark-field TPI. Successful tissue classification is demonstrated

DTMiner: identification of potential disease targets through biomedical literature mining.

Science.gov (United States)

Xu, Dong; Zhang, Meizhuo; Xie, Yanping; Wang, Fan; Chen, Ming; Zhu, Kenny Q; Wei, Jia

2016-12-01

Biomedical researchers often search through massive catalogues of literature to look for potential relationships between genes and diseases. Given the rapid growth of biomedical literature, automatic relation extraction, a crucial technology in biomedical literature mining, has shown great potential to support research of gene-related diseases. Existing work in this field has produced datasets that are limited both in scale and accuracy. In this study, we propose a reliable and efficient framework that takes large biomedical literature repositories as inputs, identifies credible relationships between diseases and genes, and presents possible genes related to a given disease and possible diseases related to a given gene. The framework incorporates name entity recognition (NER), which identifies occurrences of genes and diseases in texts, association detection whereby we extract and evaluate features from gene-disease pairs, and ranking algorithms that estimate how closely the pairs are related. The F1-score of the NER phase is 0.87, which is higher than existing studies. The association detection phase takes drastically less time than previous work while maintaining a comparable F1-score of 0.86. The end-to-end result achieves a 0.259 F1-score for the top 50 genes associated with a disease, which performs better than previous work. In addition, we released a web service for public use of the dataset. The implementation of the proposed algorithms is publicly available at http://gdr-web.rwebox.com/public_html/index.php?page=download.php The web service is available at http://gdr-web.rwebox.com/public_html/index.php CONTACT: jenny.wei@astrazeneca.com or kzhu@cs.sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Biomedical nanomaterials from design to implementation

CERN Document Server

Webster, Thomas

2016-01-01

Biomedical Nanomaterials brings together the engineering applications and challenges of using nanostructured surfaces and nanomaterials in healthcare in a single source. Each chapter covers important and new information in the biomedical applications of nanomaterials.

A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering.

Science.gov (United States)

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-04-01

Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. In the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. Experimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated Extraction Of Associations Between Methylated Genes and Diseases From Biomedical Literature

KAUST Repository

Bin Res, Arwa A.

2012-12-01

Associations between methylated genes and diseases have been investigated in several studies, and it is critical to have such information available for better understanding of diseases and clinical decisions. However, such information is scattered in a large number of electronic publications and it is difficult to manually search for it. Therefore, the goal of the project is to develop a machine learning model that can efficiently extract such information. Twelve machine learning algorithms were applied and compared in application to this problem based on three approaches that involve: document-term frequency matrices, position weight matrices, and a hybrid approach that uses the combination of the previous two. The best results we obtained by the hybrid approach with a random forest model that, in a 10-fold cross-validation, achieved F-score and accuracy of nearly 85% and 84%, respectively. On a completely separate testing set, F-score and accuracy of 89% and 88%, respectively, were obtained. Based on this model, we developed a tool that automates extraction of associations between methylated genes and diseases from electronic text. Our study contributed an efficient method for extracting specific types of associations from free text and the methodology developed here can be extended to other similar association extraction problems.

Extraction of Information of Audio-Visual Contents

Directory of Open Access Journals (Sweden)

Carlos Aguilar

2011-10-01

Full Text Available In this article we show how it is possible to use Channel Theory (Barwise and Seligman, 1997 for modeling the process of information extraction realized by audiences of audio-visual contents. To do this, we rely on the concepts pro- posed by Channel Theory and, especially, its treatment of representational systems. We then show how the information that an agent is capable of extracting from the content depends on the number of channels he is able to establish between the content and the set of classifications he is able to discriminate. The agent can endeavor the extraction of information through these channels from the totality of content; however, we discuss the advantages of extracting from its constituents in order to obtain a greater number of informational items that represent it. After showing how the extraction process is endeavored for each channel, we propose a method of representation of all the informative values an agent can obtain from a content using a matrix constituted by the channels the agent is able to establish on the content (source classifications, and the ones he can understand as individual (destination classifications. We finally show how this representation allows reflecting the evolution of the informative items through the evolution of audio-visual content.

Scenario Customization for Information Extraction

National Research Council Canada - National Science Library

Yangarber, Roman

2001-01-01

Information Extraction (IE) is an emerging NLP technology, whose function is to process unstructured, natural language text, to locate specific pieces of information, or facts, in the text, and to use these facts to fill a database...

Can we replace curation with information extraction software?

Science.gov (United States)

Karp, Peter D

2016-01-01

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

Transductive Pattern Learning for Information Extraction

National Research Council Canada - National Science Library

McLernon, Brian; Kushmerick, Nicholas

2006-01-01

.... We present TPLEX, a semi-supervised learning algorithm for information extraction that can acquire extraction patterns from a small amount of labelled text in conjunction with a large amount of unlabelled text...

Frontiers in biomedical engineering and biotechnology.

Science.gov (United States)

Liu, Feng; Goodarzi, Ali; Wang, Haifeng; Stasiak, Joanna; Sun, Jianbo; Zhou, Yu

2014-01-01

The 2nd International Conference on Biomedical Engineering and Biotechnology (iCBEB 2013), held in Wuhan on 11–13 October 2013, is an annual conference that aims at providing an opportunity for international and national researchers and practitioners to present the most recent advances and future challenges in the fields of Biomedical Information, Biomedical Engineering and Biotechnology. The papers published by this issue are selected from this conference, which witnesses the frontier in the field of Biomedical Engineering and Biotechnology, which particularly has helped improving the level of clinical diagnosis in medical work.

Mediator infrastructure for information integration and semantic data integration environment for biomedical research.

Science.gov (United States)

Grethe, Jeffrey S; Ross, Edward; Little, David; Sanders, Brian; Gupta, Amarnath; Astakhov, Vadim

2009-01-01

This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.

An integrated biomedical knowledge extraction and analysis platform: using federated search and document clustering technology.

Science.gov (United States)

Taylor, Donald P

2007-01-01

High content screening (HCS) requires time-consuming and often complex iterative information retrieval and assessment approaches to optimally conduct drug discovery programs and biomedical research. Pre- and post-HCS experimentation both require the retrieval of information from public as well as proprietary literature in addition to structured information assets such as compound libraries and projects databases. Unfortunately, this information is typically scattered across a plethora of proprietary bioinformatics tools and databases and public domain sources. Consequently, single search requests must be presented to each information repository, forcing the results to be manually integrated for a meaningful result set. Furthermore, these bioinformatics tools and data repositories are becoming increasingly complex to use; typically they fail to allow for more natural query interfaces. Vivisimo has developed an enterprise software platform to bridge disparate silos of information. The platform automatically categorizes search results into descriptive folders without the use of taxonomies to drive the categorization. A new approach to information retrieval for HCS experimentation is proposed.

Computational intelligence in biomedical imaging

CERN Document Server

2014-01-01

This book provides a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients’ medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians’ decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational inte...

Telemedicine optoelectronic biomedical data processing system

Science.gov (United States)

Prosolovska, Vita V.

2010-08-01

The telemedicine optoelectronic biomedical data processing system is created to share medical information for the control of health rights and timely and rapid response to crisis. The system includes the main blocks: bioprocessor, analog-digital converter biomedical images, optoelectronic module for image processing, optoelectronic module for parallel recording and storage of biomedical imaging and matrix screen display of biomedical images. Rated temporal characteristics of the blocks defined by a particular triggering optoelectronic couple in analog-digital converters and time imaging for matrix screen. The element base for hardware implementation of the developed matrix screen is integrated optoelectronic couples produced by selective epitaxy.

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

Science.gov (United States)

Bachman, John A; Gyori, Benjamin M; Sorger, Peter K

2018-06-28

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity

International symposium on Biomedical Data Infrastructure (BDI 2013)

CERN Document Server

Dhillon, Sarinder; Advances in biomedical infrastructure 2013

2013-01-01

Current Biomedical Databases are independently administered in geographically distinct locations, lending them almost ideally to adoption of intelligent data management approaches. This book focuses on research issues, problems and opportunities in Biomedical Data Infrastructure identifying new issues and directions for future research in Biomedical Data and Information Retrieval, Semantics in Biomedicine, and Biomedical Data Modeling and Analysis. The book will be a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development in biomedical data management.

Biomedical informatics and translational medicine

Directory of Open Access Journals (Sweden)

Sarkar Indra

2010-02-01

Full Text Available Abstract Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians" can be essential members of translational medicine teams.

Biomedical informatics: we are what we publish.

Science.gov (United States)

Elkin, P L; Brown, S H; Wright, G

2013-01-01

This article is part of a For-Discussion-Section of Methods of Information in Medicine on "Biomedical Informatics: We are what we publish". It is introduced by an editorial and followed by a commentary paper with invited comments. In subsequent issues the discussion may continue through letters to the editor. Informatics experts have attempted to define the field via consensus projects which has led to consensus statements by both AMIA. and by IMIA. We add to the output of this process the results of a study of the Pubmed publications with abstracts from the field of Biomedical Informatics. We took the terms from the AMIA consensus document and the terms from the IMIA definitions of the field of Biomedical Informatics and combined them through human review to create the Health Informatics Ontology. We built a terminology server using the Intelligent Natural Language Processor (iNLP). Then we downloaded the entire set of articles in Medline identified by searching the literature by "Medical Informatics" OR "Bioinformatics". The articles were parsed by the joint AMIA / IMIA terminology and then again using SNOMED CT and for the Bioinformatics they were also parsed using HGNC Ontology. We identified 153,580 articles using "Medical Informatics" and 20,573 articles using "Bioinformatics". This resulted in 168,298 unique articles and an overlap of 5,855 articles. Of these 62,244 articles (37%) had titles and abstracts that contained at least one concept from the Health Informatics Ontology. SNOMED CT indexing showed that the field interacts with most all clinical fields of medicine. Further defining the field by what we publish can add value to the consensus driven processes that have been the mainstay of the efforts to date. Next steps should be to extract terms from the literature that are uncovered and create class hierarchies and relationships for this content. We should also examine the high occurring of MeSH terms as markers to define Biomedical Informatics

Optical Aperture Synthesis Object's Information Extracting Based on Wavelet Denoising

International Nuclear Information System (INIS)

Fan, W J; Lu, Y

2006-01-01

Wavelet denoising is studied to improve OAS(optical aperture synthesis) object's Fourier information extracting. Translation invariance wavelet denoising based on Donoho wavelet soft threshold denoising is researched to remove Pseudo-Gibbs in wavelet soft threshold image. OAS object's information extracting based on translation invariance wavelet denoising is studied. The study shows that wavelet threshold denoising can improve the precision and the repetition of object's information extracting from interferogram, and the translation invariance wavelet denoising information extracting is better than soft threshold wavelet denoising information extracting

Improving validity of informed consent for biomedical research in Zambia using a laboratory exposure intervention.

Science.gov (United States)

Zulu, Joseph Mumba; Lisulo, Mpala Mwanza; Besa, Ellen; Kaonga, Patrick; Chisenga, Caroline C; Chomba, Mumba; Simuyandi, Michelo; Banda, Rosemary; Kelly, Paul

2014-01-01

Complex biomedical research can lead to disquiet in communities with limited exposure to scientific discussions, leading to rumours or to high drop-out rates. We set out to test an intervention designed to address apprehensions commonly encountered in a community where literacy is uncommon, and where complex biomedical research has been conducted for over a decade. We aimed to determine if it could improve the validity of consent. Data were collected using focus group discussions, key informant interviews and observations. We designed an intervention that exposed participants to a detailed demonstration of laboratory processes. Each group was interviewed twice in a day, before and after exposure to the intervention in order to assess changes in their views. Factors that motivated people to participate in invasive biomedical research included a desire to stay healthy because of the screening during the recruitment process, regular advice from doctors, free medical services, and trust in the researchers. Inhibiting factors were limited knowledge about samples taken from their bodies during endoscopic procedures, the impact of endoscopy on the function of internal organs, and concerns about the use of biomedical samples. The belief that blood can be used for Satanic practices also created insecurities about drawing of blood samples. Further inhibiting factors included a fear of being labelled as HIV positive if known to consult heath workers repeatedly, and gender inequality. Concerns about the use and storage of blood and tissue samples were overcome by a laboratory exposure intervention. Selecting a group of members from target community and engaging them in a laboratory exposure intervention could be a useful tool for enhancing specific aspects of consent for biomedical research. Further work is needed to determine the extent to which improved understanding permeates beyond the immediate group participating in the intervention.

Improving validity of informed consent for biomedical research in Zambia using a laboratory exposure intervention.

Directory of Open Access Journals (Sweden)

Joseph Mumba Zulu

Full Text Available Complex biomedical research can lead to disquiet in communities with limited exposure to scientific discussions, leading to rumours or to high drop-out rates. We set out to test an intervention designed to address apprehensions commonly encountered in a community where literacy is uncommon, and where complex biomedical research has been conducted for over a decade. We aimed to determine if it could improve the validity of consent.Data were collected using focus group discussions, key informant interviews and observations. We designed an intervention that exposed participants to a detailed demonstration of laboratory processes. Each group was interviewed twice in a day, before and after exposure to the intervention in order to assess changes in their views.Factors that motivated people to participate in invasive biomedical research included a desire to stay healthy because of the screening during the recruitment process, regular advice from doctors, free medical services, and trust in the researchers. Inhibiting factors were limited knowledge about samples taken from their bodies during endoscopic procedures, the impact of endoscopy on the function of internal organs, and concerns about the use of biomedical samples. The belief that blood can be used for Satanic practices also created insecurities about drawing of blood samples. Further inhibiting factors included a fear of being labelled as HIV positive if known to consult heath workers repeatedly, and gender inequality. Concerns about the use and storage of blood and tissue samples were overcome by a laboratory exposure intervention.Selecting a group of members from target community and engaging them in a laboratory exposure intervention could be a useful tool for enhancing specific aspects of consent for biomedical research. Further work is needed to determine the extent to which improved understanding permeates beyond the immediate group participating in the intervention.

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.

Biomedical and health informatics education and research at the Information Technology Institute in Egypt.

Science.gov (United States)

Hussein, R; Khalifa, A

2011-01-01

During the last decade, Egypt has experienced a revolution in the field of Information and Communication Technology (ICT) that has had a corresponding impact on the field of healthcare. Since 1993, the Information Technology Institute (ITI) has been leading the development of the Information Technology (IT) professional training and education in Egypt to produce top quality IT professionals who are considered now the backbone of the IT revolution in Egypt. For the past five years, ITI has been adopting the objective of building high caliber health professionals who can effectively serve the ever-growing information society. Academic links have been established with internationally renowned universities, e.g., Oregon Health and Science University (OHSU) in US, University of Leipzig in Germany, in addition those with the Egyptian Fellowship Board in order to enrich ITI Medical Informatics Education and Research. The ITI Biomedical and Health Informatics (BMHI) education and training programs target fresh graduates as well as life-long learners. Therefore, the program's learning objectives are framed within the context of the four specialization tracks: Healthcare Management (HCM), Biomedical Informatics Research (BMIR), Bioinformatics Professional (BIP), and Healthcare Professional (HCP). The ITI BMHI research projects tackle a wide-range of current challenges in this field, such as knowledge management in healthcare, providing tele-consultation services for diagnosis and treatment of infectious diseases for underserved regions in Egypt, and exploring the cultural and educational aspects of Nanoinformatics. Since 2006, ITI has been positively contributing to develop the discipline of BMHI in Egypt in order to support improved healthcare services.

Facilitating biomedical researchers' interrogation of electronic health record data: Ideas from outside of biomedical informatics.

Science.gov (United States)

Hruby, Gregory W; Matsoukas, Konstantina; Cimino, James J; Weng, Chunhua

2016-04-01

Electronic health records (EHR) are a vital data resource for research uses, including cohort identification, phenotyping, pharmacovigilance, and public health surveillance. To realize the promise of EHR data for accelerating clinical research, it is imperative to enable efficient and autonomous EHR data interrogation by end users such as biomedical researchers. This paper surveys state-of-art approaches and key methodological considerations to this purpose. We adapted a previously published conceptual framework for interactive information retrieval, which defines three entities: user, channel, and source, by elaborating on channels for query formulation in the context of facilitating end users to interrogate EHR data. We show the current progress in biomedical informatics mainly lies in support for query execution and information modeling, primarily due to emphases on infrastructure development for data integration and data access via self-service query tools, but has neglected user support needed during iteratively query formulation processes, which can be costly and error-prone. In contrast, the information science literature has offered elaborate theories and methods for user modeling and query formulation support. The two bodies of literature are complementary, implying opportunities for cross-disciplinary idea exchange. On this basis, we outline the directions for future informatics research to improve our understanding of user needs and requirements for facilitating autonomous interrogation of EHR data by biomedical researchers. We suggest that cross-disciplinary translational research between biomedical informatics and information science can benefit our research in facilitating efficient data access in life sciences. Copyright © 2016 Elsevier Inc. All rights reserved.

Pathophysiologic mechanisms of biomedical nanomaterials

International Nuclear Information System (INIS)

Wang, Liming; Chen, Chunying

2016-01-01

Nanomaterials (NMs) have been widespread used in biomedical fields, daily consuming, and even food industry. It is crucial to understand the safety and biomedical efficacy of NMs. In this review, we summarized the recent progress about the physiological and pathological effects of NMs from several levels: protein-nano interface, NM-subcellular structures, and cell–cell interaction. We focused on the detailed information of nano-bio interaction, especially about protein adsorption, intracellular trafficking, biological barriers, and signaling pathways as well as the associated mechanism mediated by nanomaterials. We also introduced related analytical methods that are meaningful and helpful for biomedical effect studies in the future. We believe that knowledge about pathophysiologic effects of NMs is not only significant for rational design of medical NMs but also helps predict their safety and further improve their applications in the future. - Highlights: • Rapid protein adsorption onto nanomaterials that affects biomedical effects • Nanomaterials and their interaction with biological membrane, intracellular trafficking and specific cellular effects • Nanomaterials and their interaction with biological barriers • The signaling pathways mediated by nanomaterials and related biomedical effects • Novel techniques for studying translocation and biomedical effects of NMs

Pathophysiologic mechanisms of biomedical nanomaterials

Energy Technology Data Exchange (ETDEWEB)

Wang, Liming, E-mail: wangliming@ihep.ac.cn; Chen, Chunying, E-mail: chenchy@nanoctr.cn

2016-05-15

Nanomaterials (NMs) have been widespread used in biomedical fields, daily consuming, and even food industry. It is crucial to understand the safety and biomedical efficacy of NMs. In this review, we summarized the recent progress about the physiological and pathological effects of NMs from several levels: protein-nano interface, NM-subcellular structures, and cell–cell interaction. We focused on the detailed information of nano-bio interaction, especially about protein adsorption, intracellular trafficking, biological barriers, and signaling pathways as well as the associated mechanism mediated by nanomaterials. We also introduced related analytical methods that are meaningful and helpful for biomedical effect studies in the future. We believe that knowledge about pathophysiologic effects of NMs is not only significant for rational design of medical NMs but also helps predict their safety and further improve their applications in the future. - Highlights: • Rapid protein adsorption onto nanomaterials that affects biomedical effects • Nanomaterials and their interaction with biological membrane, intracellular trafficking and specific cellular effects • Nanomaterials and their interaction with biological barriers • The signaling pathways mediated by nanomaterials and related biomedical effects • Novel techniques for studying translocation and biomedical effects of NMs.

Conflicts of interests and access to information resulting from biomedical research: an international legal perspective.

Science.gov (United States)

Byk, Christian

2002-07-01

Recently adopted international texts have given a new focus on conflicts of interests and access to information resulting from biomedical research. They confirmed ethical review committees as a central point to guarantee individual rights and the effective application of ethical principles. Therefore specific attention should be paid in giving such committees all the facilities necessary to keep them independent and qualified.

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines.

Science.gov (United States)

Jácome, Alberto G; Fdez-Riverola, Florentino; Lourenço, Anália

2016-07-01

meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Biomedical engineering today : An overview from the viewpoint of the German Biomedical Engineering Society].

Science.gov (United States)

Schlötelburg, C; Becks, T; Stieglitz, T

2010-08-01

Biomedical engineering is characterized by the interdisciplinary co-operation of technology, science, and ways of thinking, probably more than any other technological area. The close interaction of engineering and information sciences with medicine and biology results in innovative products and methods, but also requires high standards for the interdisciplinary transfer of ideas into products for patients' benefits. This article describes the situation of biomedical engineering in Germany. It displays characteristics of the medical device industry and ranks it with respect to the international market. The research landscape is described as well as up-to-date research topics and trends. The national funding situation of research in biomedical engineering is reviewed and existing innovation barriers are discussed.

Architecture for biomedical multimedia information delivery on the World Wide Web

Science.gov (United States)

Long, L. Rodney; Goh, Gin-Hua; Neve, Leif; Thoma, George R.

1997-10-01

Research engineers at the National Library of Medicine are building a prototype system for the delivery of multimedia biomedical information on the World Wide Web. This paper discuses the architecture and design considerations for the system, which will be used initially to make images and text from the third National Health and Nutrition Examination Survey (NHANES) publicly available. We categorized our analysis as follows: (1) fundamental software tools: we analyzed trade-offs among use of conventional HTML/CGI, X Window Broadway, and Java; (2) image delivery: we examined the use of unconventional TCP transmission methods; (3) database manager and database design: we discuss the capabilities and planned use of the Informix object-relational database manager and the planned schema for the HNANES database; (4) storage requirements for our Sun server; (5) user interface considerations; (6) the compatibility of the system with other standard research and analysis tools; (7) image display: we discuss considerations for consistent image display for end users. Finally, we discuss the scalability of the system in terms of incorporating larger or more databases of similar data, and the extendibility of the system for supporting content-based retrieval of biomedical images. The system prototype is called the Web-based Medical Information Retrieval System. An early version was built as a Java applet and tested on Unix, PC, and Macintosh platforms. This prototype used the MiniSQL database manager to do text queries on a small database of records of participants in the second NHANES survey. The full records and associated x-ray images were retrievable and displayable on a standard Web browser. A second version has now been built, also a Java applet, using the MySQL database manager.

Cause Information Extraction from Financial Articles Concerning Business Performance

Science.gov (United States)

Sakai, Hiroyuki; Masuyama, Shigeru

We propose a method of extracting cause information from Japanese financial articles concerning business performance. Our method acquires cause informtion, e. g. “_??__??__??__??__??__??__??__??__??__??_ (zidousya no uriage ga koutyou: Sales of cars were good)”. Cause information is useful for investors in selecting companies to invest. Our method extracts cause information as a form of causal expression by using statistical information and initial clue expressions automatically. Our method can extract causal expressions without predetermined patterns or complex rules given by hand, and is expected to be applied to other tasks for acquiring phrases that have a particular meaning not limited to cause information. We compared our method with our previous one originally proposed for extracting phrases concerning traffic accident causes and experimental results showed that our new method outperforms our previous one.

A Semantics-Based Approach to Retrieving Biomedical Information

DEFF Research Database (Denmark)

Andreasen, Troels; Bulskov, Henrik; Zambach, Sine

2011-01-01

This paper describes an approach to representing, organising, and accessing conceptual content of biomedical texts using a formal ontology. The ontology is based on UMLS resources supplemented with domain ontologies developed in the project. The approach introduces the notion of ‘generative ontol...... of data mining of texts identifying paraphrases and concept relations and measuring distances between key concepts in texts. Thus, the project is distinct in its attempt to provide a formal underpinning of conceptual similarity or relatedness of meaning.......This paper describes an approach to representing, organising, and accessing conceptual content of biomedical texts using a formal ontology. The ontology is based on UMLS resources supplemented with domain ontologies developed in the project. The approach introduces the notion of ‘generative...... ontologies’, i.e., ontologies providing increasingly specialised concepts reflecting the phrase structure of natural language. Furthermore, we propose a novel so called ontological semantics which maps noun phrases from texts and queries into nodes in the generative ontology. This enables an advanced form...

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain

OpenAIRE

Hassanpour, Saeed; O?Connor, Martin J; Das, Amar K

2013-01-01

Background A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based t...

Sample-based XPath Ranking for Web Information Extraction

NARCIS (Netherlands)

Jundt, Oliver; van Keulen, Maurice

Web information extraction typically relies on a wrapper, i.e., program code or a configuration that specifies how to extract some information from web pages at a specific website. Manually creating and maintaining wrappers is a cumbersome and error-prone task. It may even be prohibitive as some

Ontology-Based Information Extraction for Business Intelligence

Science.gov (United States)

Saggion, Horacio; Funk, Adam; Maynard, Diana; Bontcheva, Kalina

Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of building a complete end-to-end solution.

An Overview of Biomolecular Event Extraction from Scientific Documents.

Science.gov (United States)

Vanegas, Jorge A; Matos, Sérgio; González, Fabio; Oliveira, José L

2015-01-01

This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

An Overview of Biomolecular Event Extraction from Scientific Documents

Directory of Open Access Journals (Sweden)

Jorge A. Vanegas

2015-01-01

Full Text Available This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

BIG: a Grid Portal for Biomedical Data and Images

Directory of Open Access Journals (Sweden)

Giovanni Aloisio

2004-06-01

Full Text Available Modern management of biomedical systems involves the use of many distributed resources, such as high performance computational resources to analyze biomedical data, mass storage systems to store them, medical instruments (microscopes, tomographs, etc., advanced visualization and rendering tools. Grids offer the computational power, security and availability needed by such novel applications. This paper presents BIG (Biomedical Imaging Grid, a Web-based Grid portal for management of biomedical information (data and images in a distributed environment. BIG is an interactive environment that deals with complex user's requests, regarding the acquisition of biomedical data, the "processing" and "delivering" of biomedical images, using the power and security of Computational Grids.

Biomedical Data Mining

NARCIS (Netherlands)

Peek, N.; Combi, C.; Tucker, A.

2009-01-01

Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data

A Two-Step Resume Information Extraction Algorithm

Directory of Open Access Journals (Sweden)

Jie Chen

2018-01-01

Full Text Available With the rapid growth of Internet-based recruiting, there are a great number of personal resumes among recruiting systems. To gain more attention from the recruiters, most resumes are written in diverse formats, including varying font size, font colour, and table cells. However, the diversity of format is harmful to data mining, such as resume information extraction, automatic job matching, and candidates ranking. Supervised methods and rule-based methods have been proposed to extract facts from resumes, but they strongly rely on hierarchical structure information and large amounts of labelled data, which are hard to collect in reality. In this paper, we propose a two-step resume information extraction approach. In the first step, raw text of resume is identified as different resume blocks. To achieve the goal, we design a novel feature, Writing Style, to model sentence syntax information. Besides word index and punctuation index, word lexical attribute and prediction results of classifiers are included in Writing Style. In the second step, multiple classifiers are employed to identify different attributes of fact information in resumes. Experimental results on a real-world dataset show that the algorithm is feasible and effective.

The Agent of extracting Internet Information with Lead Order

Science.gov (United States)

Mo, Zan; Huang, Chuliang; Liu, Aijun

In order to carry out e-commerce better, advanced technologies to access business information are in need urgently. An agent is described to deal with the problems of extracting internet information that caused by the non-standard and skimble-scamble structure of Chinese websites. The agent designed includes three modules which respond to the process of extracting information separately. A method of HTTP tree and a kind of Lead algorithm is proposed to generate a lead order, with which the required web can be retrieved easily. How to transform the extracted information structuralized with natural language is also discussed.

Applications of computational intelligence in biomedical technology

CERN Document Server

Majernik, Jaroslav; Pancerz, Krzysztof; Zaitseva, Elena

2016-01-01

This book presents latest results and selected applications of Computational Intelligence in Biomedical Technologies. Most of contributions deal with problems of Biomedical and Medical Informatics, ranging from theoretical considerations to practical applications. Various aspects of development methods and algorithms in Biomedical and Medical Informatics as well as Algorithms for medical image processing, modeling methods are discussed. Individual contributions also cover medical decision making support, estimation of risks of treatments, reliability of medical systems, problems of practical clinical applications and many other topics  This book is intended for scientists interested in problems of Biomedical Technologies, for researchers and academic staff, for all dealing with Biomedical and Medical Informatics, as well as PhD students. Useful information is offered also to IT companies, developers of equipment and/or software for medicine and medical professionals.  .

A novel biomedical image indexing and retrieval system via deep preference learning.

Science.gov (United States)

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state

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

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.

Education of biomedical engineering in Taiwan.

Science.gov (United States)

Lin, Kang-Ping; Kao, Tsair; Wang, Jia-Jung; Chen, Mei-Jung; Su, Fong-Chin

2014-01-01

Biomedical Engineers (BME) play an important role in medical and healthcare society. Well educational programs are important to support the healthcare systems including hospitals, long term care organizations, manufacture industries of medical devices/instrumentations/systems, and sales/services companies of medical devices/instrumentations/system. In past 30 more years, biomedical engineering society has accumulated thousands people hold a biomedical engineering degree, and work as a biomedical engineer in Taiwan. Most of BME students can be trained in biomedical engineering departments with at least one of specialties in bioelectronics, bio-information, biomaterials or biomechanics. Students are required to have internship trainings in related institutions out of campus for 320 hours before graduating. Almost all the biomedical engineering departments are certified by IEET (Institute of Engineering Education Taiwan), and met the IEET requirement in which required mathematics and fundamental engineering courses. For BMEs after graduation, Taiwanese Society of Biomedical Engineering (TSBME) provides many continue-learning programs and certificates for all members who expect to hold the certification as a professional credit in his working place. In current status, many engineering departments in university are continuously asked to provide joint programs with BME department to train much better quality students. BME is one of growing fields in Taiwan.

Fine-grained information extraction from German transthoracic echocardiography reports.

Science.gov (United States)

Toepfer, Martin; Corovic, Hamo; Fette, Georg; Klügl, Peter; Störk, Stefan; Puppe, Frank

2015-11-12

Information extraction techniques that get structured representations out of unstructured data make a large amount of clinically relevant information about patients accessible for semantic applications. These methods typically rely on standardized terminologies that guide this process. Many languages and clinical domains, however, lack appropriate resources and tools, as well as evaluations of their applications, especially if detailed conceptualizations of the domain are required. For instance, German transthoracic echocardiography reports have not been targeted sufficiently before, despite of their importance for clinical trials. This work therefore aimed at development and evaluation of an information extraction component with a fine-grained terminology that enables to recognize almost all relevant information stated in German transthoracic echocardiography reports at the University Hospital of Würzburg. A domain expert validated and iteratively refined an automatically inferred base terminology. The terminology was used by an ontology-driven information extraction system that outputs attribute value pairs. The final component has been mapped to the central elements of a standardized terminology, and it has been evaluated according to documents with different layouts. The final system achieved state-of-the-art precision (micro average.996) and recall (micro average.961) on 100 test documents that represent more than 90 % of all reports. In particular, principal aspects as defined in a standardized external terminology were recognized with f 1=.989 (micro average) and f 1=.963 (macro average). As a result of keyword matching and restraint concept extraction, the system obtained high precision also on unstructured or exceptionally short documents, and documents with uncommon layout. The developed terminology and the proposed information extraction system allow to extract fine-grained information from German semi-structured transthoracic echocardiography reports

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

Science.gov (United States)

Xing, Yuting; Wu, Chengkun; Yang, Xi; Wang, Wei; Zhu, En; Yin, Jianping

2018-04-27

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

International Nuclear Information System (INIS)

Escudero, Javier; Hornero, Roberto; Abásolo, Daniel

2009-01-01

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings.

Science.gov (United States)

Escudero, Javier; Hornero, Roberto; Abásolo, Daniel

2009-02-01

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

Handbook of photonics for biomedical engineering

CERN Document Server

Kim, Donghyun; Somekh, Michael

2017-01-01

Nanophotonics has emerged rapidly into technological mainstream with the advent and maturity of nanotechnology available in photonics and enabled many new exciting applications in the area of biomedical science and engineering that were unimagined even a few years ago with conventional photonic engineering techniques. Handbook of Nanophotonics in Biomedical Engineering is intended to be a reliable resource to a wealth of information on nanophotonics that can inspire readers by detailing emerging and established possibilities of nanophotonics in biomedical science and engineering applications. This comprehensive reference presents not only the basics of nanophotonics but also explores recent experimental and clinical methods used in biomedical and bioengineering research. Each peer-reviewed chapter of this book discusses fundamental aspects and materials/fabrication issues of nanophotonics, as well as applications in interfaces, cell, tissue, animal studies, and clinical engineering. The organization provides ...

Statistics and Biomedical Informatics in Forensic Sciences

Czech Academy of Sciences Publication Activity Database

Zvárová, Jana

2009-01-01

Roč. 20, č. 6 (2009), s. 743-750 ISSN 1180-4009. [TIES 2007. Annual Meeting of the International Environmental Society /18./. Mikulov, 16.08.2007-20.08.2007] Institutional research plan: CEZ:AV0Z10300504 Keywords : biomedical informatics * biomedical statistics * genetic information * forensic dentistry Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.000, year: 2009

A Part-Of-Speech term weighting scheme for biomedical information retrieval.

Science.gov (United States)

Wang, Yanshan; Wu, Stephen; Li, Dingcheng; Mehrabi, Saeed; Liu, Hongfang

2016-10-01

In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users' search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature. We hypothesize that NLP can be incorporated into IR to strengthen the conventional IR models. In this study, we propose two NLP-empowered IR models, POS-BoW and POS-MRF, which incorporate automatic POS-based term weighting schemes into bag-of-word (BoW) and Markov Random Field (MRF) IR models, respectively. In the proposed models, the POS-based term weights are iteratively calculated by utilizing a cyclic coordinate method where golden section line search algorithm is applied along each coordinate to optimize the objective function defined by mean average precision (MAP). In the empirical experiments, we used the data sets from the Medical Records track in Text REtrieval Conference (TREC) 2011 and 2012 and the Genomics track in TREC 2004. The evaluation on TREC 2011 and 2012 Medical Records tracks shows that, for the POS-BoW models, the mean improvement rates for IR evaluation metrics, MAP, bpref, and P@10, are 10.88%, 4.54%, and 3.82%, compared to the BoW models; and for the POS-MRF models, these rates are 13.59%, 8.20%, and 8.78%, compared to the MRF models. Additionally, we experimentally verify that the proposed weighting approach is superior to the simple heuristic and frequency based weighting approaches, and validate our POS category selection. Using the optimal weights calculated in this experiment, we tested the proposed models on the TREC 2004 Genomics track and obtained average of 8.63% and 10.04% improvement rates for POS-BoW and POS

Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences

KAUST Repository

Sana, Furrukh

2016-11-01

Sparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the fundamental bottlenecks, both in terms of estimation accuracies and required computational resources. In the first part of dissertation, we present a high precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust performance in the presence of noise and interferences. We also present a comprehensive framework for the important problem of extracting the fetal electrocardiogram (ECG) signals from abdominal ECG recordings of pregnant women. The multiple measurement vectors approach utilized for this purpose provides an efficient mechanism of exploiting the common structure of ECG signals, when represented in sparse transform domains, and allows leveraging information from multiple ECG electrodes under a joint estimation formulation. In the second part of dissertation, we adopt sparse signal processing principles for improved information recovery in large-scale subsurface reservoir characterization problems. We propose multiple new algorithms for sparse representation of the subsurface geological structures, incorporation of useful prior information in the estimation process, and for reducing computational complexities of the problem. The techniques presented here enable significantly

PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

Directory of Open Access Journals (Sweden)

Lin Jimmy

2008-06-01

Full Text Available Abstract Background Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed® search interface, a MEDLINE® citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. Results We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. Conclusion The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain.

PageRank without hyperlinks: reranking with PubMed related article networks for biomedical text retrieval.

Science.gov (United States)

Lin, Jimmy

2008-06-06

Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed(R) search interface, a MEDLINE(R) citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain.

Bio-medical CMOS ICs

CERN Document Server

Yoo, Hoi-Jun

2011-01-01

This book is based on a graduate course entitled, Ubiquitous Healthcare Circuits and Systems, that was given by one of the editors. It includes an introduction and overview to biomedical ICs and provides information on the current trends in research.

Biomedical Imaging Principles and Applications

CERN Document Server

Salzer, Reiner

2012-01-01

This book presents and describes imaging technologies that can be used to study chemical processes and structural interactions in dynamic systems, principally in biomedical systems. The imaging technologies, largely biomedical imaging technologies such as MRT, Fluorescence mapping, raman mapping, nanoESCA, and CARS microscopy, have been selected according to their application range and to the chemical information content of their data. These technologies allow for the analysis and evaluation of delicate biological samples, which must not be disturbed during the profess. Ultimately, this may me

Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task.

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Wei, Chih-Hsuan; Peng, Yifan; Leaman, Robert; Davis, Allan Peter; Mattingly, Carolyn J; Li, Jiao; Wiegers, Thomas C; Lu, Zhiyong

2016-01-01

Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task--a result that approaches the human inter-annotator agreement (0.8875)--and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system's ability to return real-time results: the average response time for each team's DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of

Biomedical signal and image processing

CERN Document Server

Najarian, Kayvan

2012-01-01

INTRODUCTION TO DIGITAL SIGNAL AND IMAGE PROCESSINGSignals and Biomedical Signal ProcessingIntroduction and OverviewWhat is a ""Signal""?Analog, Discrete, and Digital SignalsProcessing and Transformation of SignalsSignal Processing for Feature ExtractionSome Characteristics of Digital ImagesSummaryProblemsFourier TransformIntroduction and OverviewOne-Dimensional Continuous Fourier TransformSampling and NYQUIST RateOne-Dimensional Discrete Fourier TransformTwo-Dimensional Discrete Fourier TransformFilter DesignSummaryProblemsImage Filtering, Enhancement, and RestorationIntroduction and Overview

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation.

Science.gov (United States)

Huang, Chung-Chi; Lu, Zhiyong

2016-01-01

Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as 'CHEMICAL-1 compared to CHEMICAL-2' With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical-disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked order

[Big data, medical language and biomedical terminology systems].

Science.gov (United States)

Schulz, Stefan; López-García, Pablo

2015-08-01

A variety of rich terminology systems, such as thesauri, classifications, nomenclatures and ontologies support information and knowledge processing in health care and biomedical research. Nevertheless, human language, manifested as individually written texts, persists as the primary carrier of information, in the description of disease courses or treatment episodes in electronic medical records, and in the description of biomedical research in scientific publications. In the context of the discussion about big data in biomedicine, we hypothesize that the abstraction of the individuality of natural language utterances into structured and semantically normalized information facilitates the use of statistical data analytics to distil new knowledge out of textual data from biomedical research and clinical routine. Computerized human language technologies are constantly evolving and are increasingly ready to annotate narratives with codes from biomedical terminology. However, this depends heavily on linguistic and terminological resources. The creation and maintenance of such resources is labor-intensive. Nevertheless, it is sensible to assume that big data methods can be used to support this process. Examples include the learning of hierarchical relationships, the grouping of synonymous terms into concepts and the disambiguation of homonyms. Although clear evidence is still lacking, the combination of natural language technologies, semantic resources, and big data analytics is promising.

PIMiner: A web tool for extraction of protein interactions from biomedical literature

KAUST Repository

Chowdhary, Rajesh; Zhang, Jinfeng; Tan, Sinlam; Osborne, Daniel E.; Bajic, Vladimir B.; Liu, Jun

2013-01-01

server can be accessed through a web browser or remotely through a client's command line. PIMiner can process 50,000 PubMed abstracts in approximately 7 min and thus appears suitable for large-scale processing of biological/biomedical literature

A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations

Science.gov (United States)

Bollegala, Danushka; Kontonatsios, Georgios; Ananiadou, Sophia

2015-01-01

Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as

A cross-lingual similarity measure for detecting biomedical term translations.

Directory of Open Access Journals (Sweden)

Danushka Bollegala

Full Text Available Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source from another language (target. Specifically, a biomedical term in a language is represented using two types of features: (a intrinsic features that consist of character n-grams extracted from the term under consideration, and (b extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP--a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR. The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD and non-negative matrix factorization (NMF in a nearest neighbor prediction task. Moreover, our experimental results covering several language

Biofabrication and characterization of silver nanoparticles using aqueous extract of seaweed Enteromorpha compressa and its biomedical properties

Directory of Open Access Journals (Sweden)

Vijayan Sri Ramkumar

2017-03-01

Full Text Available Green synthesis of nanoparticles using seaweeds are fascinating high research attention nowadays and also gaining center of attention in biomedical applications. In this work, we have synthesized biocompatible and functionalized silver nanoparticles using an aqueous extract of seaweed Enteromorpha compressa as a reducing as well as stabilizing agent and their efficient antimicrobial and anticancer activity are reported here. The UV–vis spectra of AgNPs showed the characteristics SPR absorption band at 421 nm. The chemical interaction and crystalline nature of the AgNPs were evaluated by FT-IR and XRD studies. The XRD result of AgNPs shows typical Ag reflection peaks at 38.1°, 44.2°, 64.4° and 77.1° corresponding to (111, (200, (220 and (311 Bragg’s planes. The surface morphology and composition of the samples were observed by HRTEM, EDS and SAED pattern analyses. Spherical shaped Ag nano structures were observed in the size ranges between 4 and 24 nm with clear lattice fringes in the HRTEM image. This report reveals that seaweed mediated synthesis of AgNPs and sustained delivery of Ag ions to the bacterial and fungal surface have been reducing their growth rate which was evaluated by well diffusion assay. The synthesized AgNPs showed favorable cytotoxicity against Ehlrich Ascites Carcinoma (EAC cells with IC50 value was recorded at 95.35 μg mL−1. This study showed cost effective silver nanoparticles synthesis with excellent biocompatibility and thus could potentially be utilized in biomedical and pharmaceutical applications.

Biomedical semantics in the Semantic Web.

Science.gov (United States)

Splendiani, Andrea; Burger, Albert; Paschke, Adrian; Romano, Paolo; Marshall, M Scott

2011-03-07

The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.

Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

Science.gov (United States)

Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

2017-05-01

The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC is an e-learning platform that empowers the biomedical community to develop, launch and share open training materials. It deploys hands-on software training toolboxes through virtualization technologies such as Amazon EC2 and Virtualbox. The BBDTC facilitates migration of courses across other course management platforms. The framework encourages knowledge sharing and content personalization through the playlist functionality that enables unique learning experiences and accelerates information dissemination to a wider community.

Integrating systems biology models and biomedical ontologies.

Science.gov (United States)

Hoehndorf, Robert; Dumontier, Michel; Gennari, John H; Wimalaratne, Sarala; de Bono, Bernard; Cook, Daniel L; Gkoutos, Georgios V

2011-08-11

Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.

KISTI at TREC 2014 Clinical Decision Support Track: Concept-based Document Re-ranking to Biomedical Information Retrieval

Science.gov (United States)

2014-11-01

sematic type. Injury or Poisoning inpo T037 Anatomical Abnormality anab T190 Given a document D, a concept vector = {1, 2, … , ...integrating biomedical terminology . Nucleic acids research 32, Database issue (2004), 267–270. 5. Chapman, W.W., Hillert, D., Velupillai, S., et...Conference (TREC), (2011). 9. Koopman, B. and Zuccon, G. Understanding negation and family history to improve clinical information retrieval. Proceedings

Review of spectral imaging technology in biomedical engineering: achievements and challenges.

Science.gov (United States)

Li, Qingli; He, Xiaofu; Wang, Yiting; Liu, Hongying; Xu, Dongrong; Guo, Fangmin

2013-10-01

Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

Extracting Information from Multimedia Meeting Collections

OpenAIRE

Gatica-Perez, Daniel; Zhang, Dong; Bengio, Samy

2005-01-01

Multimedia meeting collections, composed of unedited audio and video streams, handwritten notes, slides, and electronic documents that jointly constitute a raw record of complex human interaction processes in the workplace, have attracted interest due to the increasing feasibility of recording them in large quantities, by the opportunities for information access and retrieval applications derived from the automatic extraction of relevant meeting information, and by the challenges that the ext...

Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts.

Science.gov (United States)

Sharma, Vivekanand; Law, Wayne; Balick, Michael J; Sarkar, Indra Neil

2017-01-01

The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement.

NEMO: Extraction and normalization of organization names from PubMed affiliations.

Science.gov (United States)

Jonnalagadda, Siddhartha Reddy; Topham, Philip

2010-10-04

Today, there are more than 18 million articles related to biomedical research indexed in MEDLINE, and information derived from them could be used effectively to save the great amount of time and resources spent by government agencies in understanding the scientific landscape, including key opinion leaders and centers of excellence. Associating biomedical articles with organization names could significantly benefit the pharmaceutical marketing industry, health care funding agencies and public health officials and be useful for other scientists in normalizing author names, automatically creating citations, indexing articles and identifying potential resources or collaborators. Large amount of extracted information helps in disambiguating organization names using machine-learning algorithms. We propose NEMO, a system for extracting organization names in the affiliation and normalizing them to a canonical organization name. Our parsing process involves multi-layered rule matching with multiple dictionaries. The system achieves more than 98% f-score in extracting organization names. Our process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. A high precision was also observed in normalization. NEMO is the missing link in associating each biomedical paper and its authors to an organization name in its canonical form and the Geopolitical location of the organization. This research could potentially help in analyzing large social networks of organizations for landscaping a particular topic, improving performance of author disambiguation, adding weak links in the co-author network of authors, augmenting NLM's MARS system for correcting errors in OCR output of affiliation field, and automatically indexing the PubMed citations with the normalized organization name and country. Our system is available as a graphical user interface available for download along with this paper.

PKDE4J: Entity and relation extraction for public knowledge discovery.

Science.gov (United States)

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

An improved rank based disease prediction using web navigation patterns on bio-medical databases

Directory of Open Access Journals (Sweden)

P. Dhanalakshmi

2017-12-01

Full Text Available Applying machine learning techniques to on-line biomedical databases is a challenging task, as this data is collected from large number of sources and it is multi-dimensional. Also retrieval of relevant document from large repository such as gene document takes more processing time and an increased false positive rate. Generally, the extraction of biomedical document is based on the stream of prior observations of gene parameters taken at different time periods. Traditional web usage models such as Markov, Bayesian and Clustering models are sensitive to analyze the user navigation patterns and session identification in online biomedical database. Moreover, most of the document ranking models on biomedical database are sensitive to sparsity and outliers. In this paper, a novel user recommendation system was implemented to predict the top ranked biomedical documents using the disease type, gene entities and user navigation patterns. In this recommendation system, dynamic session identification, dynamic user identification and document ranking techniques were used to extract the highly relevant disease documents on the online PubMed repository. To verify the performance of the proposed model, the true positive rate and runtime of the model was compared with that of traditional static models such as Bayesian and Fuzzy rank. Experimental results show that the performance of the proposed ranking model is better than the traditional models.

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval.

Science.gov (United States)

Rahman, Md Mahmudur; Antani, Sameer K; Demner-Fushman, Dina; Thoma, George R

2015-10-01

This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term "concept" refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature.

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

Science.gov (United States)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

Characteristics desired in clinical data warehouse for biomedical research.

Science.gov (United States)

Shin, Soo-Yong; Kim, Woo Sung; Lee, Jae-Ho

2014-04-01

Due to the unique characteristics of clinical data, clinical data warehouses (CDWs) have not been successful so far. Specifically, the use of CDWs for biomedical research has been relatively unsuccessful thus far. The characteristics necessary for the successful implementation and operation of a CDW for biomedical research have not clearly defined yet. THREE EXAMPLES OF CDWS WERE REVIEWED: a multipurpose CDW in a hospital, a CDW for independent multi-institutional research, and a CDW for research use in an institution. After reviewing the three CDW examples, we propose some key characteristics needed in a CDW for biomedical research. A CDW for research should include an honest broker system and an Institutional Review Board approval interface to comply with governmental regulations. It should also include a simple query interface, an anonymized data review tool, and a data extraction tool. Also, it should be a biomedical research platform for data repository use as well as data analysis. The proposed characteristics desired in a CDW may have limited transfer value to organizations in other countries. However, these analysis results are still valid in Korea, and we have developed clinical research data warehouse based on these desiderata.

Integrating Information Extraction Agents into a Tourism Recommender System

Science.gov (United States)

Esparcia, Sergio; Sánchez-Anguix, Víctor; Argente, Estefanía; García-Fornes, Ana; Julián, Vicente

Recommender systems face some problems. On the one hand information needs to be maintained updated, which can result in a costly task if it is not performed automatically. On the other hand, it may be interesting to include third party services in the recommendation since they improve its quality. In this paper, we present an add-on for the Social-Net Tourism Recommender System that uses information extraction and natural language processing techniques in order to automatically extract and classify information from the Web. Its goal is to maintain the system updated and obtain information about third party services that are not offered by service providers inside the system.

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

Science.gov (United States)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Optimal Information Extraction of Laser Scanning Dataset by Scale-Adaptive Reduction

Science.gov (United States)

Zang, Y.; Yang, B.

2018-04-01

3D laser technology is widely used to collocate the surface information of object. For various applications, we need to extract a good perceptual quality point cloud from the scanned points. To solve the problem, most of existing methods extract important points based on a fixed scale. However, geometric features of 3D object come from various geometric scales. We propose a multi-scale construction method based on radial basis function. For each scale, important points are extracted from the point cloud based on their importance. We apply a perception metric Just-Noticeable-Difference to measure degradation of each geometric scale. Finally, scale-adaptive optimal information extraction is realized. Experiments are undertaken to evaluate the effective of the proposed method, suggesting a reliable solution for optimal information extraction of object.

OPTIMAL INFORMATION EXTRACTION OF LASER SCANNING DATASET BY SCALE-ADAPTIVE REDUCTION

Directory of Open Access Journals (Sweden)

Y. Zang

2018-04-01

Full Text Available 3D laser technology is widely used to collocate the surface information of object. For various applications, we need to extract a good perceptual quality point cloud from the scanned points. To solve the problem, most of existing methods extract important points based on a fixed scale. However, geometric features of 3D object come from various geometric scales. We propose a multi-scale construction method based on radial basis function. For each scale, important points are extracted from the point cloud based on their importance. We apply a perception metric Just-Noticeable-Difference to measure degradation of each geometric scale. Finally, scale-adaptive optimal information extraction is realized. Experiments are undertaken to evaluate the effective of the proposed method, suggesting a reliable solution for optimal information extraction of object.

The Ontology for Biomedical Investigations.

Science.gov (United States)

Bandrowski, Anita; Brinkman, Ryan; Brochhausen, Mathias; Brush, Matthew H; Bug, Bill; Chibucos, Marcus C; Clancy, Kevin; Courtot, Mélanie; Derom, Dirk; Dumontier, Michel; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Gibson, Frank; Gonzalez-Beltran, Alejandra; Haendel, Melissa A; He, Yongqun; Heiskanen, Mervi; Hernandez-Boussard, Tina; Jensen, Mark; Lin, Yu; Lister, Allyson L; Lord, Phillip; Malone, James; Manduchi, Elisabetta; McGee, Monnie; Morrison, Norman; Overton, James A; Parkinson, Helen; Peters, Bjoern; Rocca-Serra, Philippe; Ruttenberg, Alan; Sansone, Susanna-Assunta; Scheuermann, Richard H; Schober, Daniel; Smith, Barry; Soldatova, Larisa N; Stoeckert, Christian J; Taylor, Chris F; Torniai, Carlo; Turner, Jessica A; Vita, Randi; Whetzel, Patricia L; Zheng, Jie

2016-01-01

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.

Science.gov (United States)

Bravo, Àlex; Piñero, Janet; Queralt-Rosinach, Núria; Rautschka, Michael; Furlong, Laura I

2015-02-21

Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a

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.

Improving biomedical information retrieval by linear combinations of different query expansion techniques.

Science.gov (United States)

Abdulla, Ahmed AbdoAziz Ahmed; Lin, Hongfei; Xu, Bo; Banbhrani, Santosh Kumar

2016-07-25

Biomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the user's needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low-quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the user's information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries. The retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 %, even it outperforms a previous study by 7.12 %. We propose several query expansion techniques and their combinations (linearly) to make user queries more cognizable to search engines and to produce higher-quality search results.

Knowledge Dictionary for Information Extraction on the Arabic Text Data

Directory of Open Access Journals (Sweden)

Wahyu Jauharis Saputra

2013-04-01

Full Text Available Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%. 

Compound image segmentation of published biomedical figures.

Science.gov (United States)

Li, Pengyuan; Jiang, Xiangying; Kambhamettu, Chandra; Shatkay, Hagit

2018-04-01

Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. shatkay@udel.edu. Supplementary data are available online at Bioinformatics.

The caCORE Software Development Kit: Streamlining construction of interoperable biomedical information services

Directory of Open Access Journals (Sweden)

Warzel Denise

2006-01-01

Full Text Available Abstract Background Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs. The National Cancer Institute (NCI developed the cancer common ontologic representation environment (caCORE to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems. Results The caCORE SDK requires a Unified Modeling Language (UML tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has

Extracting Various Classes of Data From Biological Text Using the Concept of Existence Dependency.

Science.gov (United States)

Taha, Kamal

2015-11-01

One of the key goals of biological natural language processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependences. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called EDCC. EDCC aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined earlier, as follows: 1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules; and 2) it applies a semantic relationship extraction model that extracts information from different structural forms of constituents in sentences. EDCC can be used to extract different types of data from biological texts for purposes such as protein function prediction, genetic network construction, and protein-protein interaction detection. We evaluated the quality of EDCC by comparing it experimentally with six systems. Results showed marked improvement.

MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature

Directory of Open Access Journals (Sweden)

Fang Yu-Ching

2011-12-01

Full Text Available Abstract Background DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations. Description Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at http://bws.iis.sinica.edu.tw:8081/MeInfoText2/. Conclusion The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus.

Multiscale computer modeling in biomechanics and biomedical engineering

CERN Document Server

2013-01-01

This book reviews the state-of-the-art in multiscale computer modeling, in terms of both accomplishments and challenges. The information in the book is particularly useful for biomedical engineers, medical physicists and researchers in systems biology, mathematical biology, micro-biomechanics and biomaterials who are interested in how to bridge between traditional biomedical engineering work at the organ and tissue scales, and the newer arenas of cellular and molecular bioengineering.

Biomedical data integration in computational drug design and bioinformatics.

Science.gov (United States)

Seoane, Jose A; Aguiar-Pulido, Vanessa; Munteanu, Cristian R; Rivero, Daniel; Rabunal, Juan R; Dorado, Julian; Pazos, Alejandro

2013-03-01

In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.

Multi-Filter String Matching and Human-Centric Entity Matching for Information Extraction

Science.gov (United States)

Sun, Chong

2012-01-01

More and more information is being generated in text documents, such as Web pages, emails and blogs. To effectively manage this unstructured information, one broadly used approach includes locating relevant content in documents, extracting structured information and integrating the extracted information for querying, mining or further analysis. In…

Biomedical engineering and nanotechnology

International Nuclear Information System (INIS)

Pawar, S.H.; Khyalappa, R.J.; Yakhmi, J.V.

2009-01-01

This book is predominantly a compilation of papers presented in the conference which is focused on the development in biomedical materials, biomedical devises and instrumentation, biomedical effects of electromagnetic radiation, electrotherapy, radiotherapy, biosensors, biotechnology, bioengineering, tissue engineering, clinical engineering and surgical planning, medical imaging, hospital system management, biomedical education, biomedical industry and society, bioinformatics, structured nanomaterial for biomedical application, nano-composites, nano-medicine, synthesis of nanomaterial, nano science and technology development. The papers presented herein contain the scientific substance to suffice the academic directivity of the researchers from the field of biomedicine, biomedical engineering, material science and nanotechnology. Papers relevant to INIS are indexed separately

Biomedical photonics handbook biomedical diagnostics

CERN Document Server

Vo-Dinh, Tuan

2014-01-01

Shaped by Quantum Theory, Technology, and the Genomics RevolutionThe integration of photonics, electronics, biomaterials, and nanotechnology holds great promise for the future of medicine. This topic has recently experienced an explosive growth due to the noninvasive or minimally invasive nature and the cost-effectiveness of photonic modalities in medical diagnostics and therapy. The second edition of the Biomedical Photonics Handbook presents fundamental developments as well as important applications of biomedical photonics of interest to scientists, engineers, manufacturers, teachers, studen

Biomedical engineering principles

CERN Document Server

Ritter, Arthur B; Valdevit, Antonio; Ascione, Alfred N

2011-01-01

Introduction: Modeling of Physiological ProcessesCell Physiology and TransportPrinciples and Biomedical Applications of HemodynamicsA Systems Approach to PhysiologyThe Cardiovascular SystemBiomedical Signal ProcessingSignal Acquisition and ProcessingTechniques for Physiological Signal ProcessingExamples of Physiological Signal ProcessingPrinciples of BiomechanicsPractical Applications of BiomechanicsBiomaterialsPrinciples of Biomedical Capstone DesignUnmet Clinical NeedsEntrepreneurship: Reasons why Most Good Designs Never Get to MarketAn Engineering Solution in Search of a Biomedical Problem

Research on Crowdsourcing Emergency Information Extraction of Based on Events' Frame

Science.gov (United States)

Yang, Bo; Wang, Jizhou; Ma, Weijun; Mao, Xi

2018-01-01

At present, the common information extraction method cannot extract the structured emergency event information accurately; the general information retrieval tool cannot completely identify the emergency geographic information; these ways also do not have an accurate assessment of these results of distilling. So, this paper proposes an emergency information collection technology based on event framework. This technique is to solve the problem of emergency information picking. It mainly includes emergency information extraction model (EIEM), complete address recognition method (CARM) and the accuracy evaluation model of emergency information (AEMEI). EIEM can be structured to extract emergency information and complements the lack of network data acquisition in emergency mapping. CARM uses a hierarchical model and the shortest path algorithm and allows the toponomy pieces to be joined as a full address. AEMEI analyzes the results of the emergency event and summarizes the advantages and disadvantages of the event framework. Experiments show that event frame technology can solve the problem of emergency information drawing and provides reference cases for other applications. When the emergency disaster is about to occur, the relevant departments query emergency's data that has occurred in the past. They can make arrangements ahead of schedule which defense and reducing disaster. The technology decreases the number of casualties and property damage in the country and world. This is of great significance to the state and society.

A rapid extraction of landslide disaster information research based on GF-1 image

Science.gov (United States)

Wang, Sai; Xu, Suning; Peng, Ling; Wang, Zhiyi; Wang, Na

2015-08-01

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 .Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

Should biomedical research be like Airbnb?

Science.gov (United States)

Bonazzi, Vivien R; Bourne, Philip E

2017-04-01

The thesis presented here is that biomedical research is based on the trusted exchange of services. That exchange would be conducted more efficiently if the trusted software platforms to exchange those services, if they exist, were more integrated. While simpler and narrower in scope than the services governing biomedical research, comparison to existing internet-based platforms, like Airbnb, can be informative. We illustrate how the analogy to internet-based platforms works and does not work and introduce The Commons, under active development at the National Institutes of Health (NIH) and elsewhere, as an example of the move towards platforms for research.

For 481 biomedical open access journals, articles are not searchable in the Directory of Open Access Journals nor in conventional biomedical databases.

Science.gov (United States)

Liljekvist, Mads Svane; Andresen, Kristoffer; Pommergaard, Hans-Christian; Rosenberg, Jacob

2015-01-01

Background. Open access (OA) journals allows access to research papers free of charge to the reader. Traditionally, biomedical researchers use databases like MEDLINE and EMBASE to discover new advances. However, biomedical OA journals might not fulfill such databases' criteria, hindering dissemination. The Directory of Open Access Journals (DOAJ) is a database exclusively listing OA journals. The aim of this study was to investigate DOAJ's coverage of biomedical OA journals compared with the conventional biomedical databases. Methods. Information on all journals listed in four conventional biomedical databases (MEDLINE, PubMed Central, EMBASE and SCOPUS) and DOAJ were gathered. Journals were included if they were (1) actively publishing, (2) full OA, (3) prospectively indexed in one or more database, and (4) of biomedical subject. Impact factor and journal language were also collected. DOAJ was compared with conventional databases regarding the proportion of journals covered, along with their impact factor and publishing language. The proportion of journals with articles indexed by DOAJ was determined. Results. In total, 3,236 biomedical OA journals were included in the study. Of the included journals, 86.7% were listed in DOAJ. Combined, the conventional biomedical databases listed 75.0% of the journals; 18.7% in MEDLINE; 36.5% in PubMed Central; 51.5% in SCOPUS and 50.6% in EMBASE. Of the journals in DOAJ, 88.7% published in English and 20.6% had received impact factor for 2012 compared with 93.5% and 26.0%, respectively, for journals in the conventional biomedical databases. A subset of 51.1% and 48.5% of the journals in DOAJ had articles indexed from 2012 and 2013, respectively. Of journals exclusively listed in DOAJ, one journal had received an impact factor for 2012, and 59.6% of the journals had no content from 2013 indexed in DOAJ. Conclusions. DOAJ is the most complete registry of biomedical OA journals compared with five conventional biomedical databases

Archives of Medical and Biomedical Research: Submissions

African Journals Online (AJOL)

Author Guidelines. INFORMATION FOR CONTRIBUTORS This information can also be accessed at http://www.iambr.info/AMBR/author_guidelines.html Articles to Archives of Medical and Biomedical Research are submitted under the condition that the work described has not been published or is not being considered for ...

Information Extraction with Character-level Neural Networks and Free Noisy Supervision

OpenAIRE

Meerkamp, Philipp; Zhou, Zhengyi

2016-01-01

We present an architecture for information extraction from text that augments an existing parser with a character-level neural network. The network is trained using a measure of consistency of extracted data with existing databases as a form of noisy supervision. Our architecture combines the ability of constraint-based information extraction systems to easily incorporate domain knowledge and constraints with the ability of deep neural networks to leverage large amounts of data to learn compl...

Unsupervised information extraction by text segmentation

CERN Document Server

Cortez, Eli

2013-01-01

A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors' approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a

Biomedical engineering fundamentals

CERN Document Server

Bronzino, Joseph D

2014-01-01

Known as the bible of biomedical engineering, The Biomedical Engineering Handbook, Fourth Edition, sets the standard against which all other references of this nature are measured. As such, it has served as a major resource for both skilled professionals and novices to biomedical engineering.Biomedical Engineering Fundamentals, the first volume of the handbook, presents material from respected scientists with diverse backgrounds in physiological systems, biomechanics, biomaterials, bioelectric phenomena, and neuroengineering. More than three dozen specific topics are examined, including cardia

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

2018-01-01

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. PMID:27807747

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.

KNODWAT: a scientific framework application for testing knowledge discovery methods for the biomedical domain.

Science.gov (United States)

Holzinger, Andreas; Zupan, Mario

2013-06-13

Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework.

Extraction of CT dose information from DICOM metadata: automated Matlab-based approach.

Science.gov (United States)

Dave, Jaydev K; Gingold, Eric L

2013-01-01

The purpose of this study was to extract exposure parameters and dose-relevant indexes of CT examinations from information embedded in DICOM metadata. DICOM dose report files were identified and retrieved from a PACS. An automated software program was used to extract from these files information from the structured elements in the DICOM metadata relevant to exposure. Extracting information from DICOM metadata eliminated potential errors inherent in techniques based on optical character recognition, yielding 100% accuracy.

Journal of Medicine and Biomedical Research - Vol 14, No 1 (2015)

African Journals Online (AJOL)

Journal of Medicine and Biomedical Research - Vol 14, No 1 (2015) ... Histopathological effects of oral and subcutaneous administration of Roselle Calyx ... Ameliorative effect of Vitamin C on lead induced hepatotoxicty in rats · EMAIL ... Effect of Hibiscus sabdariffa calyx extract on stressed rabbit plasma cholesterol status ...

Advanced applications of natural language processing for performing information extraction

CERN Document Server

Rodrigues, Mário

2015-01-01

This book explains how can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web. Readers are introduced to the problem of IE and its current challenges and limitations, supported with examples. The book discusses the need to fill the gap between documents, data, and people, and provides a broad overview of the technology supporting IE. The authors present a generic architecture for developing systems that are able to learn how to extract relevant information from natural language documents, and illustrate how to implement working systems using state-of-the-art and freely available software tools. The book also discusses concrete applications illustrating IE uses.   ·         Provides an overview of state-of-the-art technology in information extraction (IE), discussing achievements and limitations for t...

SOA-BD: Service Oriented Architecture for Biomedical Devices

Directory of Open Access Journals (Sweden)

João Marcos Teixeira Lacerda

2017-05-01

Full Text Available Introduction: The communication of information systems with biomedical devices has become complex not only due to the existence of several private communication protocols, but also to the immutable way that software is embedded into these devices. In this sense, this paper proposes a service-oriented architecture to access biomedical devices as a way to abstract the mechanisms of writing and reading data from these devices, thus contributing to enable the focus of the development team of biomedical software to be intended for its functional requirements, i.e. business rules relevant to the problem domain. Methods The SOA-BD architecture consists of five main components: A Web Service for transport and conversion of the device data, Communication Protocols to access the devices, Data Parsers to preprocess data, a Device Repository to store data and transmitted information and Error handling, for error handling of these information. For the development of SOA-BD, technologies such as the XML language and the Java programming language were used. Besides, Software Engineering concepts such as Design Patterns were also used. For the validation of this work, data has been collected from vital sign monitors in an Intensive Care Unit using HL7 standards. Results The tests obtained a difference of about only 1 second in terms of response time with the use of SOA-BD. Conclusion SOA-BD achieves important results such as the reduction on the access protocol complexity, the opportunity for treating patients over long distances, allowing easier development of monitoring applications and interoperability with biomedical devices from diverse manufacturers.

Post-processing of Deep Web Information Extraction Based on Domain Ontology

Directory of Open Access Journals (Sweden)

PENG, T.

2013-11-01

Full Text Available Many methods are utilized to extract and process query results in deep Web, which rely on the different structures of Web pages and various designing modes of databases. However, some semantic meanings and relations are ignored. So, in this paper, we present an approach for post-processing deep Web query results based on domain ontology which can utilize the semantic meanings and relations. A block identification model (BIM based on node similarity is defined to extract data blocks that are relevant to specific domain after reducing noisy nodes. Feature vector of domain books is obtained by result set extraction model (RSEM based on vector space model (VSM. RSEM, in combination with BIM, builds the domain ontology on books which can not only remove the limit of Web page structures when extracting data information, but also make use of semantic meanings of domain ontology. After extracting basic information of Web pages, a ranking algorithm is adopted to offer an ordered list of data records to users. Experimental results show that BIM and RSEM extract data blocks and build domain ontology accurately. In addition, relevant data records and basic information are extracted and ranked. The performances precision and recall show that our proposed method is feasible and efficient.

KaBOB: ontology-based semantic integration of biomedical databases.

Science.gov (United States)

Livingston, Kevin M; Bada, Michael; Baumgartner, William A; Hunter, Lawrence E

2015-04-23

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for

DEXTER: Disease-Expression Relation Extraction from Text.

Science.gov (United States)

Gupta, Samir; Dingerdissen, Hayley; Ross, Karen E; Hu, Yu; Wu, Cathy H; Mazumder, Raja; Vijay-Shanker, K

2018-01-01

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung

Character-level neural network for biomedical named entity recognition.

Science.gov (United States)

Gridach, Mourad

2017-06-01

Biomedical named entity recognition (BNER), which extracts important named entities such as genes and proteins, is a challenging task in automated systems that mine knowledge in biomedical texts. The previous state-of-the-art systems required large amounts of task-specific knowledge in the form of feature engineering, lexicons and data pre-processing to achieve high performance. In this paper, we introduce a novel neural network architecture that benefits from both word- and character-level representations automatically, by using a combination of bidirectional long short-term memory (LSTM) and conditional random field (CRF) eliminating the need for most feature engineering tasks. We evaluate our system on two datasets: JNLPBA corpus and the BioCreAtIvE II Gene Mention (GM) corpus. We obtained state-of-the-art performance by outperforming the previous systems. To the best of our knowledge, we are the first to investigate the combination of deep neural networks, CRF, word embeddings and character-level representation in recognizing biomedical named entities. Copyright © 2017 Elsevier Inc. All rights reserved.

Data Analysis and Data Mining: Current Issues in Biomedical Informatics

Science.gov (United States)

Bellazzi, Riccardo; Diomidous, Marianna; Sarkar, Indra Neil; Takabayashi, Katsuhiko; Ziegler, Andreas; McCray, Alexa T.

2011-01-01

Summary Background Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Objectives To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, that reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. Results The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Conclusions Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers. PMID:22146916

Should biomedical research be like Airbnb?

Directory of Open Access Journals (Sweden)

Vivien R Bonazzi

2017-04-01

Full Text Available The thesis presented here is that biomedical research is based on the trusted exchange of services. That exchange would be conducted more efficiently if the trusted software platforms to exchange those services, if they exist, were more integrated. While simpler and narrower in scope than the services governing biomedical research, comparison to existing internet-based platforms, like Airbnb, can be informative. We illustrate how the analogy to internet-based platforms works and does not work and introduce The Commons, under active development at the National Institutes of Health (NIH and elsewhere, as an example of the move towards platforms for research.

MedTime: a temporal information extraction system for clinical narratives.

Science.gov (United States)

Lin, Yu-Kai; Chen, Hsinchun; Brown, Randall A

2013-12-01

Temporal information extraction from clinical narratives is of critical importance to many clinical applications. We participated in the EVENT/TIMEX3 track of the 2012 i2b2 clinical temporal relations challenge, and presented our temporal information extraction system, MedTime. MedTime comprises a cascade of rule-based and machine-learning pattern recognition procedures. It achieved a micro-averaged f-measure of 0.88 in both the recognitions of clinical events and temporal expressions. We proposed and evaluated three time normalization strategies to normalize relative time expressions in clinical texts. The accuracy was 0.68 in normalizing temporal expressions of dates, times, durations, and frequencies. This study demonstrates and evaluates the integration of rule-based and machine-learning-based approaches for high performance temporal information extraction from clinical narratives. Copyright © 2013 Elsevier Inc. All rights reserved.

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

Science.gov (United States)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Finding and accessing diagrams in biomedical publications.

Science.gov (United States)

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts.

DKIE: Open Source Information Extraction for Danish

DEFF Research Database (Denmark)

Derczynski, Leon; Field, Camilla Vilhelmsen; Bøgh, Kenneth Sejdenfaden

2014-01-01

Danish is a major Scandinavian language spoken daily by around six million people. However, it lacks a unified, open set of NLP tools. This demonstration will introduce DKIE, an extensible open-source toolkit for processing Danish text. We implement an information extraction architecture for Danish...

Introduction to biomedical engineering

CERN Document Server

Enderle, John D; Blanchard, Susan M

2005-01-01

Under the direction of John Enderle, Susan Blanchard and Joe Bronzino, leaders in the field have contributed chapters on the most relevant subjects for biomedical engineering students. These chapters coincide with courses offered in all biomedical engineering programs so that it can be used at different levels for a variety of courses of this evolving field. Introduction to Biomedical Engineering, Second Edition provides a historical perspective of the major developments in the biomedical field. Also contained within are the fundamental principles underlying biomedical engineering design, analysis, and modeling procedures. The numerous examples, drill problems and exercises are used to reinforce concepts and develop problem-solving skills making this book an invaluable tool for all biomedical students and engineers. New to this edition: Computational Biology, Medical Imaging, Genomics and Bioinformatics. * 60% update from first edition to reflect the developing field of biomedical engineering * New chapters o...

Transliteration normalization for Information Extraction and Machine Translation

Directory of Open Access Journals (Sweden)

Yuval Marton

2014-12-01

Full Text Available Foreign name transliterations typically include multiple spelling variants. These variants cause data sparseness and inconsistency problems, increase the Out-of-Vocabulary (OOV rate, and present challenges for Machine Translation, Information Extraction and other natural language processing (NLP tasks. This work aims to identify and cluster name spelling variants using a Statistical Machine Translation method: word alignment. The variants are identified by being aligned to the same “pivot” name in another language (the source-language in Machine Translation settings. Based on word-to-word translation and transliteration probabilities, as well as the string edit distance metric, names with similar spellings in the target language are clustered and then normalized to a canonical form. With this approach, tens of thousands of high-precision name transliteration spelling variants are extracted from sentence-aligned bilingual corpora in Arabic and English (in both languages. When these normalized name spelling variants are applied to Information Extraction tasks, improvements over strong baseline systems are observed. When applied to Machine Translation tasks, a large improvement potential is shown.

End-to-end information extraction without token-level supervision

DEFF Research Database (Denmark)

Palm, Rasmus Berg; Hovy, Dirk; Laws, Florian

2017-01-01

Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels...... and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction...

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.

Science.gov (United States)

Amith, Muhammad; He, Zhe; Bian, Jiang; Lossio-Ventura, Juan Antonio; Tao, Cui

2018-04-01

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies. Copyright © 2018 Elsevier Inc. All rights reserved.

[Evidence-based medicine. 2. Research of clinically relevant biomedical information. Gruppo Italiano per la Medicina Basata sulle Evidenze--GIMBE].

Science.gov (United States)

Cartabellotta, A

1998-05-01

Evidence-based Medicine is a product of the electronic information age and there are several databases useful for practice it--MEDLINE, EMBASE, specialized compendiums of evidence (Cochrane Library, Best Evidence), practice guidelines--most of them free available through Internet, that offers a growing number of health resources. Because searching best evidence is a basic step to practice Evidence-based Medicine, this second review (the first one has been published in the issue of March 1998) has the aim to provide physicians tools and skills for retrieving relevant biomedical information. Therefore, we discuss about strategies for managing information overload, analyze characteristics, usefulness and limits of medical databases and explain how to use MEDLINE in day-to-day clinical practice.

MIMI: Multimodality, Multiresource, Information Integration Environment for Biomedical Core Facilities

OpenAIRE

Szymanski, Jacek; Wilson, David L.; Zhang, Guo-Qiang

2007-01-01

The rapid expansion of biomedical research has brought substantial scientific and administrative data management challenges to modern core facilities. Scientifically, a core facility must be able to manage experimental workflow and the corresponding set of large and complex scientific data. It must also disseminate experimental data to relevant researchers in a secure and expedient manner that facilitates collaboration and provides support for data interpretation and analysis. Administrativel...

An overview of biomedical literature search on the World Wide Web in the third millennium.

Science.gov (United States)

Kumar, Prince; Goel, Roshni; Jain, Chandni; Kumar, Ashish; Parashar, Abhishek; Gond, Ajay Ratan

2012-06-01

Complete access to the existing pool of biomedical literature and the ability to "hit" upon the exact information of the relevant specialty are becoming essential elements of academic and clinical expertise. With the rapid expansion of the literature database, it is almost impossible to keep up to date with every innovation. Using the Internet, however, most people can freely access this literature at any time, from almost anywhere. This paper highlights the use of the Internet in obtaining valuable biomedical research information, which is mostly available from journals, databases, textbooks and e-journals in the form of web pages, text materials, images, and so on. The authors present an overview of web-based resources for biomedical researchers, providing information about Internet search engines (e.g., Google), web-based bibliographic databases (e.g., PubMed, IndMed) and how to use them, and other online biomedical resources that can assist clinicians in reaching well-informed clinical decisions.

Disclosing discourses: biomedical and hospitality discourses in patient education materials.

Science.gov (United States)

Öresland, Stina; Friberg, Febe; Määttä, Sylvia; Öhlen, Joakim

2015-09-01

Patient education materials have the potential to strengthen the health literacy of patients. Previous studies indicate that readability and suitability may be improved. The aim of this study was to explore and analyze discourses inherent in patient education materials since analysis of discourses could illuminate values and norms inherent in them. Clinics in Sweden that provided colorectal cancer surgery allowed access to written information and 'welcome letters' sent to patients. The material was analysed by means of discourse analysis, embedded in Derrida's approach of deconstruction. The analysis revealed a biomedical discourse and a hospitality discourse. In the biomedical discourse, the subject position of the personnel was interpreted as the messenger of medical information while that of the patients as the carrier of diagnoses and recipients of biomedical information. In the hospitality discourse, the subject position of the personnel was interpreted as hosts who invite and welcome the patients as guests. The study highlights the need to eliminate paternalism and fosters a critical reflective stance among professionals regarding power and paternalism inherent in health care communication. © 2015 John Wiley & Sons Ltd.

Challenges for automatically extracting molecular interactions from full-text articles.

Science.gov (United States)

McIntosh, Tara; Curran, James R

2009-09-24

The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles. We manually annotated passages from full-text articles that describe interactions summarised in a Molecular Interaction Map (MIM). Our corpus tracks the process of identifying facts to form the MIM summaries and captures any factual dependencies that must be resolved to extract the fact completely. For example, a fact in the results section may require a synonym defined in the introduction. The passages are also annotated with negated and coreference expressions that must be resolved.We describe the guidelines for identifying relevant passages and possible dependencies. The corpus includes 2162 sentences from 78 full-text articles. Our corpus analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies the proportion of interaction statements requiring coherent dependencies. Further, it allows us to report on the relative importance of identifying synonyms and resolving negated expressions. We also experiment with an oracle sentence retrieval system using the corpus as a gold-standard evaluation set. We introduce the MIM corpus, a unique resource that maps interaction facts in a MIM to annotated passages within full-text articles. It is an invaluable case study providing guidance to developers of biomedical IR and IE systems, and can be used as a gold-standard evaluation set for full-text IR tasks.

Mining knowledge from text repositories using information extraction ...

Indian Academy of Sciences (India)

Information extraction (IE); text mining; text repositories; knowledge discovery from .... general purpose English words. However ... of precision and recall, as extensive experimentation is required due to lack of public tagged corpora. 4. Mining ...

Normalizing biomedical terms by minimizing ambiguity and variability

Directory of Open Access Journals (Sweden)

McNaught John

2008-04-01

Full Text Available Abstract Background One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach. Results We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS. Conclusions The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known.

The BioIntelligence Framework: a new computational platform for biomedical knowledge computing.

Science.gov (United States)

Farley, Toni; Kiefer, Jeff; Lee, Preston; Von Hoff, Daniel; Trent, Jeffrey M; Colbourn, Charles; Mousses, Spyro

2013-01-01

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

Towards an information extraction and knowledge formation framework based on Shannon entropy

Directory of Open Access Journals (Sweden)

Iliescu Dragoș

2017-01-01

Full Text Available Information quantity subject is approached in this paperwork, considering the specific domain of nonconforming product management as information source. This work represents a case study. Raw data were gathered from a heavy industrial works company, information extraction and knowledge formation being considered herein. Involved method for information quantity estimation is based on Shannon entropy formula. Information and entropy spectrum are decomposed and analysed for extraction of specific information and knowledge-that formation. The result of the entropy analysis point out the information needed to be acquired by the involved organisation, this being presented as a specific knowledge type.

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

Science.gov (United States)

Quesada-Martínez, M; Fernández-Breis, J T; Stevens, R; Mikroyannidi, E

2015-01-01

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

Tagline: Information Extraction for Semi-Structured Text Elements in Medical Progress Notes

Science.gov (United States)

Finch, Dezon Kile

2012-01-01

Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in…

Mars Target Encyclopedia: Information Extraction for Planetary Science

Science.gov (United States)

Wagstaff, K. L.; Francis, R.; Gowda, T.; Lu, Y.; Riloff, E.; Singh, K.

2017-06-01

Mars surface targets / and published compositions / Seek and ye will find. We used text mining methods to extract information from LPSC abstracts about the composition of Mars surface targets. Users can search by element, mineral, or target.

Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

Science.gov (United States)

Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

2016-06-01

The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC collaborative is an e-learning platform that supports the biomedical community to access, develop and deploy open training materials. The BBDTC supports Big Data skill training for biomedical scientists at all levels, and from varied backgrounds. The natural hierarchy of courses allows them to be broken into and handled as modules . Modules can be reused in the context of multiple courses and reshuffled, producing a new and different, dynamic course called a playlist . Users may create playlists to suit their learning requirements and share it with individual users or the wider public. BBDTC leverages the maturity and design of the HUBzero content-management platform for delivering educational content. To facilitate the migration of existing content, the BBDTC supports importing and exporting course material from the edX platform. Migration tools will be extended in the future to support other platforms. Hands-on training software packages, i.e., toolboxes , are supported through Amazon EC2 and Virtualbox virtualization technologies, and they are available as: ( i ) downloadable lightweight Virtualbox Images providing a standardized software tool environment with software packages and test data on their personal machines, and ( ii ) remotely accessible Amazon EC2 Virtual Machines for accessing biomedical big data tools and scalable big data experiments. At the moment, the BBDTC site contains three open Biomedical big data training courses with lecture contents, videos and hands-on training utilizing VM toolboxes, covering diverse topics. The courses have enhanced the hands-on learning environment by providing structured content that users can use at their own pace. A four course biomedical big data series is

Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems

Directory of Open Access Journals (Sweden)

Yangzhe Liao

2018-02-01

Full Text Available Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs. In this paper, a mutual information (MI-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.

BioSig: the free and open source software library for biomedical signal processing.

Science.gov (United States)

Vidaurre, Carmen; Sander, Tilmann H; Schlögl, Alois

2011-01-01

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Handbook on advanced design and manufacturing technologies for biomedical devices

CERN Document Server

2013-01-01

The last decades have seen remarkable advances in computer-aided design, engineering and manufacturing technologies, multi-variable simulation tools, medical imaging, biomimetic design, rapid prototyping, micro and nanomanufacturing methods and information management resources, all of which provide new horizons for the Biomedical Engineering fields and the Medical Device Industry. Handbook on Advanced Design and Manufacturing Technologies for Biomedical Devices covers such topics in depth, with an applied perspective and providing several case studies that help to analyze and understand the key factors of the different stages linked to the development of a novel biomedical device, from the conceptual and design steps, to the prototyping and industrialization phases. Main research challenges and future potentials are also discussed, taking into account relevant social demands and a growing market already exceeding billions of dollars. In time, advanced biomedical devices will decisively change methods and resu...

Optimum detection for extracting maximum information from symmetric qubit sets

International Nuclear Information System (INIS)

Mizuno, Jun; Fujiwara, Mikio; Sasaki, Masahide; Akiba, Makoto; Kawanishi, Tetsuya; Barnett, Stephen M.

2002-01-01

We demonstrate a class of optimum detection strategies for extracting the maximum information from sets of equiprobable real symmetric qubit states of a single photon. These optimum strategies have been predicted by Sasaki et al. [Phys. Rev. A 59, 3325 (1999)]. The peculiar aspect is that the detections with at least three outputs suffice for optimum extraction of information regardless of the number of signal elements. The cases of ternary (or trine), quinary, and septenary polarization signals are studied where a standard von Neumann detection (a projection onto a binary orthogonal basis) fails to access the maximum information. Our experiments demonstrate that it is possible with present technologies to attain about 96% of the theoretical limit

Study on methods and techniques of aeroradiometric weak information extraction for sandstone-hosted uranium deposits based on GIS

International Nuclear Information System (INIS)

Han Shaoyang; Ke Dan; Hou Huiqun

2005-01-01

The weak information extraction is one of the important research contents in the current sandstone-type uranium prospecting in China. This paper introduces the connotation of aeroradiometric weak information extraction, and discusses the formation theories of aeroradiometric weak information extraction, and discusses the formation theories of aeroradiometric weak information and establishes some effective mathematic models for weak information extraction. Models for weak information extraction are realized based on GIS software platform. Application tests of weak information extraction are realized based on GIS software platform. Application tests of weak information extraction are completed in known uranium mineralized areas. Research results prove that the prospective areas of sandstone-type uranium deposits can be rapidly delineated by extracting aeroradiometric weak information. (authors)

Repeat: a framework to assess empirical reproducibility in biomedical research

Directory of Open Access Journals (Sweden)

Leslie D. McIntosh

2017-09-01

Full Text Available Abstract Background The reproducibility of research is essential to rigorous science, yet significant concerns of the reliability and verifiability of biomedical research have been recently highlighted. Ongoing efforts across several domains of science and policy are working to clarify the fundamental characteristics of reproducibility and to enhance the transparency and accessibility of research. Methods The aim of the proceeding work is to develop an assessment tool operationalizing key concepts of research transparency in the biomedical domain, specifically for secondary biomedical data research using electronic health record data. The tool (RepeAT was developed through a multi-phase process that involved coding and extracting recommendations and practices for improving reproducibility from publications and reports across the biomedical and statistical sciences, field testing the instrument, and refining variables. Results RepeAT includes 119 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation. Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT. Conclusions The use of RepeAT may allow the biomedical community to have a better understanding of the current practices of research transparency and accessibility among principal investigators. Common adoption of RepeAT may improve reporting of research practices and the availability of research outputs. Additionally, use of RepeAT will facilitate comparisons of research transparency and accessibility across domains and institutions.

People with low back pain perceive needs for non-biomedical services in workplace, financial, social and household domains: a systematic review

Directory of Open Access Journals (Sweden)

Louisa Chou

2018-04-01

Full Text Available Question: What needs of non-biomedical services are perceived by people with low back pain? Design: Systematic review of qualitative and quantitative studies examining perceived needs of non-biomedical services for low back pain, identified through searching of MEDLINE, EMBASE, CINAHL and PsycINFO (1990 to 2016. Participants: Adults with low back pain of any duration. Data extraction and analysis: Descriptive data regarding study design and methodology were extracted. The preferences, expectations and satisfaction with non-biomedical services reported by people with low back pain were identified and categorised within areas of perceived need. Results: Twenty studies (19 qualitative and one quantitative involving 522 unique participants (total pool of 590 were included in this systematic review. Four areas emerged. Workplace: people with low back pain experience pressure to return to work despite difficulties with the demands of their occupation. They want their employers to be informed about low back pain and they desire workplace accommodations. Financial: people with low back pain want financial support, but have concerns about the inefficiencies of compensation systems and the stigma associated with financial remuneration. Social: people with low back pain report feeling disconnected from social networks and want back-specific social support. Household: people with low back pain report difficulties with household duties; however, there are few data regarding their need for auxiliary devices and domestic help. Conclusion: People with low back pain identified work place, financial and social pressures, and difficulties with household duties as areas of need beyond their healthcare requirements that affect their ability to comply with management of their condition. Consideration of such needs may inform physiotherapists, the wider health system, social networks and the workplace to provide more relevant and effective services. [Chou L, Cicuttini

Fundamental of biomedical engineering

CERN Document Server

Sawhney, GS

2007-01-01

About the Book: A well set out textbook explains the fundamentals of biomedical engineering in the areas of biomechanics, biofluid flow, biomaterials, bioinstrumentation and use of computing in biomedical engineering. All these subjects form a basic part of an engineer''s education. The text is admirably suited to meet the needs of the students of mechanical engineering, opting for the elective of Biomedical Engineering. Coverage of bioinstrumentation, biomaterials and computing for biomedical engineers can meet the needs of the students of Electronic & Communication, Electronic & Instrumenta

Information sources in biomedical science and medical journalism: methodological approaches and assessment.

Science.gov (United States)

Miranda, Giovanna F; Vercellesi, Luisa; Bruno, Flavia

2004-09-01

Throughout the world the public is showing increasing interest in medical and scientific subjects and journalists largely spread this information, with an important impact on knowledge and health. Clearly, therefore, the relationship between the journalist and his sources is delicate: freedom and independence of information depend on the independence and truthfulness of the sources. The new "precision journalism" holds that scientific methods should be applied to journalism, so authoritative sources are a common need for journalists and scientists. We therefore compared the individual classifications and methods of assessing of sources in biomedical science and medical journalism to try to extrapolate scientific methods of evaluation to journalism. In journalism and science terms used to classify sources of information show some similarities, but their meanings are different. In science primary and secondary classes of information, for instance, refer to the levels of processing, but in journalism to the official nature of the source itself. Scientists and journalists must both always consult as many sources as possible and check their authoritativeness, reliability, completeness, up-to-dateness and balance. In journalism, however, there are some important differences and limits: too many sources can sometimes diminish the quality of the information. The sources serve a first filter between the event and the journalist, who is not providing the reader with the fact, but with its projection. Journalists have time constraints and lack the objective criteria for searching, the specific background knowledge, and the expertise to fully assess sources. To assist in understanding the wealth of sources of information in journalism, we have prepared a checklist of items and questions. There are at least four fundamental points that a good journalist, like any scientist, should know: how to find the latest information (the sources), how to assess it (the quality and

Review of Biomedical Image Processing

Directory of Open Access Journals (Sweden)

Ciaccio Edward J

2011-11-01

Full Text Available Abstract This article is a review of the book: 'Biomedical Image Processing', by Thomas M. Deserno, which is published by Springer-Verlag. Salient information that will be useful to decide whether the book is relevant to topics of interest to the reader, and whether it might be suitable as a course textbook, are presented in the review. This includes information about the book details, a summary, the suitability of the text in course and research work, the framework of the book, its specific content, and conclusions.

Facilities available for biomedical science research in the public universities in Lagos, Nigeria.

Science.gov (United States)

John, T A

2010-03-01

Across the world, basic medical scientists and physician scientists work on common platforms in state-of-the-arts laboratories doing translational research that occasionally results in bedside application. Biotechnology industries capitalise on useful findings for colossal profit.1 In Nigeria and the rest of Africa, biomedical science has not thrived and the contribution of publications to global high impact journals is low.2 This work investigated facilities available for modern biomedical research in Lagos public universities to extract culprit factors. The two public universities in Lagos, Nigeria were investigated by a cross sectional questionnaire survey of the technical staff manning biomedical science departments. They were asked about availability of 47 modern biomedical science research laboratory components such as cold room and microscopes and six research administration components such as director of research and grants administration. For convenient basic laboratory components such as autoclaves and balances, 50% responses indicated "well maintained and always functional" whereas for less convenient complex, high maintenance, state-of-the-arts equipment 19% responses indicated "well maintained and always functional." Respondents indicated that components of modern biomedical science research administration were 44% of expectation. The survey reveal a deficit in state-of the-arts research equipment and also a deficit in high maintenance, expensive equipment indicating that biomedical science in the investigated environment lacks the momentum of global trends and also lacks buoyant funding. In addition, administration supporting biomedical science is below expectation and may also account for the low contributions of research articles to global high impact journals.

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain.

Science.gov (United States)

Hassanpour, Saeed; O'Connor, Martin J; Das, Amar K

2013-08-12

A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text. Using an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test. Our work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

NARCIS (Netherlands)

Habib, Mena Badieh; van Keulen, Maurice

2011-01-01

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration

The biomedical disciplines and the structure of biomedical and clinical knowledge.

Science.gov (United States)

Nederbragt, H

2000-11-01

The relation between biomedical knowledge and clinical knowledge is discussed by comparing their respective structures. The knowledge of a disease as a biological phenomenon is constructed by the interaction of facts and theories from the main biomedical disciplines: epidemiology, diagnostics, clinical trial, therapy development and pathogenesis. Although these facts and theories are based on probabilities and extrapolations, the interaction provides a reliable and coherent structure, comparable to a Kuhnian paradigma. In the structure of clinical knowledge, i.e. knowledge of the patient with the disease, not only biomedical knowledge contributes to the structure but also economic and social relations, ethics and personal experience. However, the interaction between each of the participating "knowledges" in clinical knowledge is not based on mutual dependency and accumulation of different arguments from each, as in biomedical knowledge, but on competition and partial exclusion. Therefore, the structure of biomedical knowledge is different from that of clinical knowledge. This difference is used as the basis for a discussion in which the place of technology, evidence-based medicine and the gap between scientific and clinical knowledge are evaluated.

Information extraction from muon radiography data

International Nuclear Information System (INIS)

Borozdin, K.N.; Asaki, T.J.; Chartrand, R.; Hengartner, N.W.; Hogan, G.E.; Morris, C.L.; Priedhorsky, W.C.; Schirato, R.C.; Schultz, L.J.; Sottile, M.J.; Vixie, K.R.; Wohlberg, B.E.; Blanpied, G.

2004-01-01

Scattering muon radiography was proposed recently as a technique of detection and 3-d imaging for dense high-Z objects. High-energy cosmic ray muons are deflected in matter in the process of multiple Coulomb scattering. By measuring the deflection angles we are able to reconstruct the configuration of high-Z material in the object. We discuss the methods for information extraction from muon radiography data. Tomographic methods widely used in medical images have been applied to a specific muon radiography information source. Alternative simple technique based on the counting of high-scattered muons in the voxels seems to be efficient in many simulated scenes. SVM-based classifiers and clustering algorithms may allow detection of compact high-Z object without full image reconstruction. The efficiency of muon radiography can be increased using additional informational sources, such as momentum estimation, stopping power measurement, and detection of muonic atom emission.

MIMI: multimodality, multiresource, information integration environment for biomedical core facilities.

Science.gov (United States)

Szymanski, Jacek; Wilson, David L; Zhang, Guo-Qiang

2009-10-01

The rapid expansion of biomedical research has brought substantial scientific and administrative data management challenges to modern core facilities. Scientifically, a core facility must be able to manage experimental workflow and the corresponding set of large and complex scientific data. It must also disseminate experimental data to relevant researchers in a secure and expedient manner that facilitates collaboration and provides support for data interpretation and analysis. Administratively, a core facility must be able to manage the scheduling of its equipment and to maintain a flexible and effective billing system to track material, resource, and personnel costs and charge for services to sustain its operation. It must also have the ability to regularly monitor the usage and performance of its equipment and to provide summary statistics on resources spent on different categories of research. To address these informatics challenges, we introduce a comprehensive system called MIMI (multimodality, multiresource, information integration environment) that integrates the administrative and scientific support of a core facility into a single web-based environment. We report the design, development, and deployment experience of a baseline MIMI system at an imaging core facility and discuss the general applicability of such a system in other types of core facilities. These initial results suggest that MIMI will be a unique, cost-effective approach to addressing the informatics infrastructure needs of core facilities and similar research laboratories.

Biomedical signals, imaging, and informatics

CERN Document Server

Bronzino, Joseph D

2014-01-01

Known as the bible of biomedical engineering, The Biomedical Engineering Handbook, Fourth Edition, sets the standard against which all other references of this nature are measured. As such, it has served as a major resource for both skilled professionals and novices to biomedical engineering.Biomedical Signals, Imaging, and Informatics, the third volume of the handbook, presents material from respected scientists with diverse backgrounds in biosignal processing, medical imaging, infrared imaging, and medical informatics.More than three dozen specific topics are examined, including biomedical s

Action GRID: assessing the impact of Nanotechnology on biomedical informatics.

Science.gov (United States)

Lopez-Alonso, Victoria; Hermosilla-Gimeno, Isabel; Lopez-Campos, Guillermo; Maojo, Victor; Martin-Sanchez, Fernando J

2008-11-06

Recent advances in Nanotechnology are slowly extending their influence in biomedical research and clinical practice (nanomedicine). The authors have recently been granted with an European Commission research project, Action-GRID. This initiative will review current developments in nanomedicine, and analyze the area of nanoinformatics. Its main outcome will be the identification of needs and the discussion of future challenges and priorities for Biomedical Informatics in terms of information processing in nanomedicine and regenerative medicine.

Powering biomedical devices

CERN Document Server

Romero, Edwar

2013-01-01

From exoskeletons to neural implants, biomedical devices are no less than life-changing. Compact and constant power sources are necessary to keep these devices running efficiently. Edwar Romero's Powering Biomedical Devices reviews the background, current technologies, and possible future developments of these power sources, examining not only the types of biomedical power sources available (macro, mini, MEMS, and nano), but also what they power (such as prostheses, insulin pumps, and muscular and neural stimulators), and how they work (covering batteries, biofluids, kinetic and ther

Terahertz Imaging for Biomedical Applications Pattern Recognition and Tomographic Reconstruction

CERN Document Server

Yin, Xiaoxia; Abbott, Derek

2012-01-01

Terahertz Imaging for Biomedical Applications: Pattern Recognition and Tomographic Reconstruction presents the necessary algorithms needed to assist screening, diagnosis, and treatment, and these algorithms will play a critical role in the accurate detection of abnormalities present in biomedical imaging. Terahertz biomedical imaging has become an area of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized with an increasing number of trials performed in a biomedical setting. Terahertz tomographic imaging and detection technology contributes to the ability to identify opaque objects with clear boundaries,and would be useful to both in vivo and ex vivo environments. This book also: Introduces terahertz radiation techniques and provides a number of topical examples of signal and image processing, as well as machine learning Presents the most recent developments in an emerging field, terahertz radiation Utilizes new methods...

The National Center for Biomedical Ontology: Advancing Biomedicinethrough Structured Organization of Scientific Knowledge

Energy Technology Data Exchange (ETDEWEB)

Rubin, Daniel L.; Lewis, Suzanna E.; Mungall, Chris J.; Misra,Sima; Westerfield, Monte; Ashburner, Michael; Sim, Ida; Chute,Christopher G.; Solbrig, Harold; Storey, Margaret-Anne; Smith, Barry; Day-Richter, John; Noy, Natalya F.; Musen, Mark A.

2006-01-23

The National Center for Biomedical Ontology (http://bioontology.org) is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists funded by the NIH Roadmap to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are: (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. The Center is working toward these objectives by providing tools to develop ontologies and to annotate experimental data, and by developing resources to integrate and relate existing ontologies as well as by creating repositories of biomedical data that are annotated using those ontologies. The Center is providing training workshops in ontology design, development, and usage, and is also pursuing research in ontology evaluation, quality, and use of ontologies to promote scientific discovery. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.

Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature.

Science.gov (United States)

Lee, Dong-Gi; Shin, Hyunjung

2017-05-18

Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature. To identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature. We applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method. This research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.

Magnetic Helical Micro- and Nanorobots: Toward Their Biomedical Applications

Directory of Open Access Journals (Sweden)

Famin Qiu

2015-03-01

Full Text Available Magnetic helical micro- and nanorobots can perform 3D navigation in various liquids with a sub-micrometer precision under low-strength rotating magnetic fields (<10 mT. Since magnetic fields with low strengths are harmless to cells and tissues, magnetic helical micro/nanorobots are promising tools for biomedical applications, such as minimally invasive surgery, cell manipulation and analysis, and targeted therapy. This review provides general information on magnetic helical micro/nanorobots, including their fabrication, motion control, and further functionalization for biomedical applications.

Recognition techniques for extracting information from semistructured documents

Science.gov (United States)

Della Ventura, Anna; Gagliardi, Isabella; Zonta, Bruna

2000-12-01

Archives of optical documents are more and more massively employed, the demand driven also by the new norms sanctioning the legal value of digital documents, provided they are stored on supports that are physically unalterable. On the supply side there is now a vast and technologically advanced market, where optical memories have solved the problem of the duration and permanence of data at costs comparable to those for magnetic memories. The remaining bottleneck in these systems is the indexing. The indexing of documents with a variable structure, while still not completely automated, can be machine supported to a large degree with evident advantages both in the organization of the work, and in extracting information, providing data that is much more detailed and potentially significant for the user. We present here a system for the automatic registration of correspondence to and from a public office. The system is based on a general methodology for the extraction, indexing, archiving, and retrieval of significant information from semi-structured documents. This information, in our prototype application, is distributed among the database fields of sender, addressee, subject, date, and body of the document.

Anethum Graveolens Linn (Umbelliferae) Extract Attenuates Stress ...

African Journals Online (AJOL)

Erah

College of Biomedical and Health Sciences, Department of Biotechnology, Konkuk University, Chungju, Republic of ... anti-stress and cognition-improving effects of A. graveolens extract in a rat model. ..... This work was supported by Konkuk.

A Literature Survey on Wireless Power Transfer for Biomedical Devices

Directory of Open Access Journals (Sweden)

Reem Shadid

2018-01-01

Full Text Available This paper provides a review and survey of research on power transfer for biomedical applications based on inductive coupling. There is interest in wireless power transfer (WPT for implantable and wearable biomedical devices, for example, heart pacemaker or implantable electrocardiogram (ECG recorders. This paper concentrates on the applications based on near-field power transfer methods, summarizes the main design features in the recent literature, and provides some information about the system model and coil optimization.

Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.

Science.gov (United States)

Doing-Harris, Kristina; Livnat, Yarden; Meystre, Stephane

2015-01-01

We develop medical-specialty specific ontologies that contain the settled science and common term usage. We leverage current practices in information and relationship extraction to streamline the ontology development process. Our system combines different text types with information and relationship extraction techniques in a low overhead modifiable system. Our SEmi-Automated ontology Maintenance (SEAM) system features a natural language processing pipeline for information extraction. Synonym and hierarchical groups are identified using corpus-based semantics and lexico-syntactic patterns. The semantic vectors we use are term frequency by inverse document frequency and context vectors. Clinical documents contain the terms we want in an ontology. They also contain idiosyncratic usage and are unlikely to contain the linguistic constructs associated with synonym and hierarchy identification. By including both clinical and biomedical texts, SEAM can recommend terms from those appearing in both document types. The set of recommended terms is then used to filter the synonyms and hierarchical relationships extracted from the biomedical corpus. We demonstrate the generality of the system across three use cases: ontologies for acute changes in mental status, Medically Unexplained Syndromes, and echocardiogram summary statements. Across the three uses cases, we held the number of recommended terms relatively constant by changing SEAM's parameters. Experts seem to find more than 300 recommended terms to be overwhelming. The approval rate of recommended terms increased as the number and specificity of clinical documents in the corpus increased. It was 60% when there were 199 clinical documents that were not specific to the ontology domain and 90% when there were 2879 documents very specific to the target domain. We found that fewer than 100 recommended synonym groups were also preferred. Approval rates for synonym recommendations remained low varying from 43% to 25% as the

User needs analysis and usability assessment of DataMed - a biomedical data discovery index.

Science.gov (United States)

Dixit, Ram; Rogith, Deevakar; Narayana, Vidya; Salimi, Mandana; Gururaj, Anupama; Ohno-Machado, Lucila; Xu, Hua; Johnson, Todd R

2017-11-30

To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources. We conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers' information and user interface needs. Biomedical researchers' information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery. Biomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process. While available data and researchers' information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers' information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Status of Research in Biomedical Engineering 1968.

Science.gov (United States)

National Inst. of General Medical Sciences (NIH), Bethesda, MD.

This status report is divided into eight sections. The first four represent the classical engineering or building aspects of bioengineering and deal with biomedical instrumentation, prosthetics, man-machine systems and computer and information systems. The next three sections are related to the scientific, intellectual and academic influence of…

Biomedical ontologies: toward scientific debate.

Science.gov (United States)

Maojo, V; Crespo, J; García-Remesal, M; de la Iglesia, D; Perez-Rey, D; Kulikowski, C

2011-01-01

Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective. We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies. We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies. Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.

RESEARCH ON REMOTE SENSING GEOLOGICAL INFORMATION EXTRACTION BASED ON OBJECT ORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

H. Gao

2018-04-01

Full Text Available The northern Tibet belongs to the Sub cold arid climate zone in the plateau. It is rarely visited by people. The geological working conditions are very poor. However, the stratum exposures are good and human interference is very small. Therefore, the research on the automatic classification and extraction of remote sensing geological information has typical significance and good application prospect. Based on the object-oriented classification in Northern Tibet, using the Worldview2 high-resolution remote sensing data, combined with the tectonic information and image enhancement, the lithological spectral features, shape features, spatial locations and topological relations of various geological information are excavated. By setting the threshold, based on the hierarchical classification, eight kinds of geological information were classified and extracted. Compared with the existing geological maps, the accuracy analysis shows that the overall accuracy reached 87.8561 %, indicating that the classification-oriented method is effective and feasible for this study area and provides a new idea for the automatic extraction of remote sensing geological information.

Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration.

Science.gov (United States)

Myneni, Sahiti; Patel, Vimla L; Bova, G Steven; Wang, Jian; Ackerman, Christopher F; Berlinicke, Cynthia A; Chen, Steve H; Lindvall, Mikael; Zack, Donald J

2016-04-01

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Discovering gene annotations in biomedical text databases

Directory of Open Access Journals (Sweden)

Ozsoyoglu Gultekin

2008-03-01

Full Text Available Abstract Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i automating the annotation of genomic entities with Gene Ontology concepts, and (ii providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate

Terrain Extraction by Integrating Terrestrial Laser Scanner Data and Spectral Information

Science.gov (United States)

Lau, C. L.; Halim, S.; Zulkepli, M.; Azwan, A. M.; Tang, W. L.; Chong, A. K.

2015-10-01

The extraction of true terrain points from unstructured laser point cloud data is an important process in order to produce an accurate digital terrain model (DTM). However, most of these spatial filtering methods just utilizing the geometrical data to discriminate the terrain points from nonterrain points. The point cloud filtering method also can be improved by using the spectral information available with some scanners. Therefore, the objective of this study is to investigate the effectiveness of using the three-channel (red, green and blue) of the colour image captured from built-in digital camera which is available in some Terrestrial Laser Scanner (TLS) for terrain extraction. In this study, the data acquisition was conducted at a mini replica landscape in Universiti Teknologi Malaysia (UTM), Skudai campus using Leica ScanStation C10. The spectral information of the coloured point clouds from selected sample classes are extracted for spectral analysis. The coloured point clouds which within the corresponding preset spectral threshold are identified as that specific feature point from the dataset. This process of terrain extraction is done through using developed Matlab coding. Result demonstrates that a higher spectral resolution passive image is required in order to improve the output. This is because low quality of the colour images captured by the sensor contributes to the low separability in spectral reflectance. In conclusion, this study shows that, spectral information is capable to be used as a parameter for terrain extraction.

Position-aware deep multi-task learning for drug-drug interaction extraction.

Science.gov (United States)

Zhou, Deyu; Miao, Lei; He, Yulan

2018-05-01

A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Extracting Semantic Information from Visual Data: A Survey

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Qiang Liu

2016-03-01

Full Text Available The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods.

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

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Martínez-Romero, Marcos; Jonquet, Clement; O'Connor, Martin J; Graybeal, John; Pazos, Alejandro; Musen, Mark A

2017-06-07

Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability

Double-compression method for biomedical images

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Antonenko, Yevhenii A.; Mustetsov, Timofey N.; Hamdi, Rami R.; Małecka-Massalska, Teresa; Orshubekov, Nurbek; DzierŻak, RóŻa; Uvaysova, Svetlana

2017-08-01

This paper describes a double compression method (DCM) of biomedical images. A comparison of image compression factors in size JPEG, PNG and developed DCM was carried out. The main purpose of the DCM - compression of medical images while maintaining the key points that carry diagnostic information. To estimate the minimum compression factor an analysis of the coding of random noise image is presented.

Crowdsourcing biomedical research: leveraging communities as innovation engines.

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Saez-Rodriguez, Julio; Costello, James C; Friend, Stephen H; Kellen, Michael R; Mangravite, Lara; Meyer, Pablo; Norman, Thea; Stolovitzky, Gustavo

2016-07-15

The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

Nigerian Journal of Health and Biomedical Sciences: Editorial Policies

African Journals Online (AJOL)

Biomedical Engineering Biotechnology in relation to Medicine Clinical Sciences Dental Sciences Environment and Health Health Economics and Management Health Information Management Hygiene and Health Education Legal Aspects of Healthcare Medical Education Nursing Sciences Pharmaceutical Sciences

Figure mining for biomedical research.

Science.gov (United States)

Rodriguez-Esteban, Raul; Iossifov, Ivan

2009-08-15

Figures from biomedical articles contain valuable information difficult to reach without specialized tools. Currently, there is no search engine that can retrieve specific figure types. This study describes a retrieval method that takes advantage of principles in image understanding, text mining and optical character recognition (OCR) to retrieve figure types defined conceptually. A search engine was developed to retrieve tables and figure types to aid computational and experimental research. http://iossifovlab.cshl.edu/figurome/.

KneeTex: an ontology-driven system for information extraction from MRI reports.

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Spasić, Irena; Zhao, Bo; Jones, Christopher B; Button, Kate

2015-01-01

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated

Biomedical databases: protecting privacy and promoting research.

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Wylie, Jean E; Mineau, Geraldine P

2003-03-01

When combined with medical information, large electronic databases of information that identify individuals provide superlative resources for genetic, epidemiology and other biomedical research. Such research resources increasingly need to balance the protection of privacy and confidentiality with the promotion of research. Models that do not allow the use of such individual-identifying information constrain research; models that involve commercial interests raise concerns about what type of access is acceptable. Researchers, individuals representing the public interest and those developing regulatory guidelines must be involved in an ongoing dialogue to identify practical models.

Improving ethical and participatory practice for marginalized populations in biomedical HIV prevention trials: lessons from Thailand.

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Dan Allman

Full Text Available BACKGROUND: This paper presents findings from a qualitative investigation of ethical and participatory issues related to the conduct of biomedical HIV prevention trials among marginalized populations in Thailand. This research was deemed important to conduct, as several large-scale biomedical HIV prevention trials among marginalized populations had closed prematurely in other countries, and a better understanding of how to prevent similar trial closures from occurring in the future was desired. METHODS: In-depth key informant interviews were held in Bangkok and Chiang Mai, Thailand. Interviews were audio recorded, transcribed, translated and thematically analyzed. The Good Participatory Practice Guidelines for Biomedical HIV Prevention Trials (GPP guided this work. RESULTS: Fourteen interviews were conducted: 10 with policymakers, academic and community-based researchers and trial staff and four with representatives of non-governmental organizations (NGOs. Suggested ways to improve ethical and participatory practice centered on standards of HIV prevention, informed consent, communication and human rights. In particular, the need to overcome language and literacy differences was identified. Key informants felt communication was the basis of ethical understanding and trust within biomedical HIV prevention trial contexts, and thus fundamental to trial participants' ability to exercise free will. DISCUSSION: Biomedical HIV prevention trials present opportunities for inclusive and productive ethical and participatory practice. Key informants suggested that efforts to improve practice could result in better relationships between research stakeholders and research investigative teams and by extension, better, more ethical participatory trials. This research took place in Thailand and its findings apply primarily to Thailand. However, given the universality of many ethical considerations, the results of this study can inform the improvement of ethical

Improving ethical and participatory practice for marginalized populations in biomedical HIV prevention trials: lessons from Thailand.

Science.gov (United States)

Allman, Dan; Ditmore, Melissa Hope; Kaplan, Karyn

2014-01-01

This paper presents findings from a qualitative investigation of ethical and participatory issues related to the conduct of biomedical HIV prevention trials among marginalized populations in Thailand. This research was deemed important to conduct, as several large-scale biomedical HIV prevention trials among marginalized populations had closed prematurely in other countries, and a better understanding of how to prevent similar trial closures from occurring in the future was desired. In-depth key informant interviews were held in Bangkok and Chiang Mai, Thailand. Interviews were audio recorded, transcribed, translated and thematically analyzed. The Good Participatory Practice Guidelines for Biomedical HIV Prevention Trials (GPP) guided this work. Fourteen interviews were conducted: 10 with policymakers, academic and community-based researchers and trial staff and four with representatives of non-governmental organizations (NGOs). Suggested ways to improve ethical and participatory practice centered on standards of HIV prevention, informed consent, communication and human rights. In particular, the need to overcome language and literacy differences was identified. Key informants felt communication was the basis of ethical understanding and trust within biomedical HIV prevention trial contexts, and thus fundamental to trial participants' ability to exercise free will. Biomedical HIV prevention trials present opportunities for inclusive and productive ethical and participatory practice. Key informants suggested that efforts to improve practice could result in better relationships between research stakeholders and research investigative teams and by extension, better, more ethical participatory trials. This research took place in Thailand and its findings apply primarily to Thailand. However, given the universality of many ethical considerations, the results of this study can inform the improvement of ethical and participatory practice in other parts of the world where

PhysiomeSpace: digital library service for biomedical data.

Science.gov (United States)

Testi, Debora; Quadrani, Paolo; Viceconti, Marco

2010-06-28

Every research laboratory has a wealth of biomedical data locked up, which, if shared with other experts, could dramatically improve biomedical and healthcare research. With the PhysiomeSpace service, it is now possible with a few clicks to share with selected users biomedical data in an easy, controlled and safe way. The digital library service is managed using a client-server approach. The client application is used to import, fuse and enrich the data information according to the PhysiomeSpace resource ontology and upload/download the data to the library. The server services are hosted on the Biomed Town community portal, where through a web interface, the user can complete the metadata curation and share and/or publish the data resources. A search service capitalizes on the domain ontology and on the enrichment of metadata for each resource, providing a powerful discovery environment. Once the users have found the data resources they are interested in, they can add them to their basket, following a metaphor popular in e-commerce web sites. When all the necessary resources have been selected, the user can download the basket contents into the client application. The digital library service is now in beta and open to the biomedical research community.

A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method.

Science.gov (United States)

Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol

2007-11-27

A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries.

Extracting the information backbone in online system.

Directory of Open Access Journals (Sweden)

Qian-Ming Zhang

Full Text Available Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such "less can be more" feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.

Extracting the information backbone in online system.

Science.gov (United States)

Zhang, Qian-Ming; Zeng, An; Shang, Ming-Sheng

2013-01-01

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such "less can be more" feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.

Extracting the Information Backbone in Online System

Science.gov (United States)

Zhang, Qian-Ming; Zeng, An; Shang, Ming-Sheng

2013-01-01

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency. PMID:23690946

Lessons Learned from Development of De-identification System for Biomedical Research in a Korean Tertiary Hospital.

Science.gov (United States)

Shin, Soo-Yong; Lyu, Yongman; Shin, Yongdon; Choi, Hyo Joung; Park, Jihyun; Kim, Woo-Sung; Lee, Jae Ho

2013-06-01

The Korean government has enacted two laws, namely, the Personal Information Protection Act and the Bioethics and Safety Act to prevent the unauthorized use of medical information. To protect patients' privacy by complying with governmental regulations and improve the convenience of research, Asan Medical Center has been developing a de-identification system for biomedical research. We reviewed Korean regulations to define the scope of the de-identification methods and well-known previous biomedical research platforms to extract the functionalities of the systems. Based on these review results, we implemented necessary programs based on the Asan Medical Center Information System framework which was built using the Microsoft. NET Framework and C#. The developed de-identification system comprises three main components: a de-identification tool, a search tool, and a chart review tool. The de-identification tool can substitute a randomly assigned research ID for a hospital patient ID, remove the identifiers in the structured format, and mask them in the unstructured format, i.e., texts. This tool achieved 98.14% precision and 97.39% recall for 6,520 clinical notes. The search tool can find the number of patients which satisfies given search criteria. The chart review tool can provide de-identified patient's clinical data for review purposes. We found that a clinical data warehouse was essential for successful implementation of the de-identification system, and this system should be tightly linked to an electronic Institutional Review Board system for easy operation of honest brokers. Additionally, we found that a secure cloud environment could be adopted to protect patients' privacy more thoroughly.

Stemcell Information: SKIP000698 [SKIP Stemcell Database[Archive

Lifescience Database Archive (English)

Full Text Available lf Jaenisch Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Research Whitehead Institute for Biomedica...l Research Whitehead Institute for Biomedical Research R...udolf Jaenisch Rudolf Jaenisch Information Only Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical

Stemcell Information: SKIP000699 [SKIP Stemcell Database[Archive

Lifescience Database Archive (English)

Full Text Available lf Jaenisch Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Research Whitehead Institute for Biomedica...l Research Whitehead Institute for Biomedical Research R...udolf Jaenisch Rudolf Jaenisch Information Only Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical

Stemcell Information: SKIP000700 [SKIP Stemcell Database[Archive

Lifescience Database Archive (English)

Full Text Available lf Jaenisch Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Research Whitehead Institute for Biomedica...l Research Whitehead Institute for Biomedical Research R...udolf Jaenisch Rudolf Jaenisch Information Only Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical

Are we studying what matters? Health priorities and NIH-funded biomedical engineering research.

Science.gov (United States)

Rubin, Jessica B; Paltiel, A David; Saltzman, W Mark

2010-07-01

With the founding of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) in 1999, the National Institutes of Health (NIH) made explicit its dedication to expanding research in biomedical engineering. Ten years later, we sought to examine how closely federal funding for biomedical engineering aligns with U.S. health priorities. Using a publicly accessible database of research projects funded by the NIH in 2008, we identified 641 grants focused on biomedical engineering, 48% of which targeted specific diseases. Overall, we found that these disease-specific NIH-funded biomedical engineering research projects align with national health priorities, as quantified by three commonly utilized measures of disease burden: cause of death, disability-adjusted survival losses, and expenditures. However, we also found some illnesses (e.g., cancer and heart disease) for which the number of research projects funded deviated from our expectations, given their disease burden. Our findings suggest several possibilities for future studies that would serve to further inform the allocation of limited research dollars within the field of biomedical engineering.

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.

Advanced Methods of Biomedical Signal Processing

CERN Document Server

Cerutti, Sergio

2011-01-01

This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications. The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as mult

Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease

Directory of Open Access Journals (Sweden)

Lang François-Michel

2006-06-01

Full Text Available Abstract Background Genomic functional information is valuable for biomedical research. However, such information frequently needs to be extracted from the scientific literature and structured in order to be exploited by automatic systems. Natural language processing is increasingly used for this purpose although it inherently involves errors. A postprocessing strategy that selects relations most likely to be correct is proposed and evaluated on the output of SemGen, a system that extracts semantic predications on the etiology of genetic diseases. Based on the number of intervening phrases between an argument and its predicate, we defined a heuristic strategy to filter the extracted semantic relations according to their likelihood of being correct. We also applied this strategy to relations identified with co-occurrence processing. Finally, we exploited postprocessed SemGen predications to investigate the genetic basis of Parkinson's disease. Results The filtering procedure for increased precision is based on the intuition that arguments which occur close to their predicate are easier to identify than those at a distance. For example, if gene-gene relations are filtered for arguments at a distance of 1 phrase from the predicate, precision increases from 41.95% (baseline to 70.75%. Since this proximity filtering is based on syntactic structure, applying it to the results of co-occurrence processing is useful, but not as effective as when applied to the output of natural language processing. In an effort to exploit SemGen predications on the etiology of disease after increasing precision with postprocessing, a gene list was derived from extracted information enhanced with postprocessing filtering and was automatically annotated with GFINDer, a Web application that dynamically retrieves functional and phenotypic information from structured biomolecular resources. Two of the genes in this list are likely relevant to Parkinson's disease but are not

Extraction Method for Earthquake-Collapsed Building Information Based on High-Resolution Remote Sensing

International Nuclear Information System (INIS)

Chen, Peng; Wu, Jian; Liu, Yaolin; Wang, Jing

2014-01-01

At present, the extraction of earthquake disaster information from remote sensing data relies on visual interpretation. However, this technique cannot effectively and quickly obtain precise and efficient information for earthquake relief and emergency management. Collapsed buildings in the town of Zipingpu after the Wenchuan earthquake were used as a case study to validate two kinds of rapid extraction methods for earthquake-collapsed building information based on pixel-oriented and object-oriented theories. The pixel-oriented method is based on multi-layer regional segments that embody the core layers and segments of the object-oriented method. The key idea is to mask layer by layer all image information, including that on the collapsed buildings. Compared with traditional techniques, the pixel-oriented method is innovative because it allows considerably rapid computer processing. As for the object-oriented method, a multi-scale segment algorithm was applied to build a three-layer hierarchy. By analyzing the spectrum, texture, shape, location, and context of individual object classes in different layers, the fuzzy determined rule system was established for the extraction of earthquake-collapsed building information. We compared the two sets of results using three variables: precision assessment, visual effect, and principle. Both methods can extract earthquake-collapsed building information quickly and accurately. The object-oriented method successfully overcomes the pepper salt noise caused by the spectral diversity of high-resolution remote sensing data and solves the problem of same object, different spectrums and that of same spectrum, different objects. With an overall accuracy of 90.38%, the method achieves more scientific and accurate results compared with the pixel-oriented method (76.84%). The object-oriented image analysis method can be extensively applied in the extraction of earthquake disaster information based on high-resolution remote sensing

Towards precision medicine; a new biomedical cosmology.

Science.gov (United States)

Vegter, M W

2018-02-10

Precision Medicine has become a common label for data-intensive and patient-driven biomedical research. Its intended future is reflected in endeavours such as the Precision Medicine Initiative in the USA. This article addresses the question whether it is possible to discern a new 'medical cosmology' in Precision Medicine, a concept that was developed by Nicholas Jewson to describe comprehensive transformations involving various dimensions of biomedical knowledge and practice, such as vocabularies, the roles of patients and physicians and the conceptualisation of disease. Subsequently, I will elaborate my assessment of the features of Precision Medicine with the help of Michel Foucault, by exploring how precision medicine involves a transformation along three axes: the axis of biomedical knowledge, of biomedical power and of the patient as a self. Patients are encouraged to become the managers of their own health status, while the medical domain is reframed as a data-sharing community, characterised by changing power relationships between providers and patients, producers and consumers. While the emerging Precision Medicine cosmology may surpass existing knowledge frameworks; it obscures previous traditions and reduces research-subjects to mere data. This in turn, means that the individual is both subjected to the neoliberal demand to share personal information, and at the same time has acquired the positive 'right' to become a member of the data-sharing community. The subject has to constantly negotiate the meaning of his or her data, which can either enable self-expression, or function as a commanding Superego.

Improving information extraction using a probability-based approach

DEFF Research Database (Denmark)

Kim, S.; Ahmed, Saeema; Wallace, K.

2007-01-01

Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, the industrial world is now more transient and key personnel move to other companies...... or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A keyword-based search is commonly used, but studies have shown...... the recall, while maintaining the high precision, a learning approach that makes identification decisions based on a probability model, rather than simply looking up the presence of the pre-defined variations, looks promising. This paper presents the results of developing such a probability-based entity...

Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development.

Science.gov (United States)

McEntire, Robin; Szalkowski, Debbie; Butler, James; Kuo, Michelle S; Chang, Meiping; Chang, Man; Freeman, Darren; McQuay, Sarah; Patel, Jagruti; McGlashen, Michael; Cornell, Wendy D; Xu, Jinghai James

2016-05-01

External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction. We believe such an efficient and secure approach provides a competitive advantage to companies needing access to the latest information for a range of use cases and complements manually curated commercial sources. Copyright © 2016. Published by Elsevier Ltd.

Handbook of biomedical optics

CERN Document Server

Boas, David A

2011-01-01

Biomedical optics holds tremendous promise to deliver effective, safe, non- or minimally invasive diagnostics and targeted, customizable therapeutics. Handbook of Biomedical Optics provides an in-depth treatment of the field, including coverage of applications for biomedical research, diagnosis, and therapy. It introduces the theory and fundamentals of each subject, ensuring accessibility to a wide multidisciplinary readership. It also offers a view of the state of the art and discusses advantages and disadvantages of various techniques.Organized into six sections, this handbook: Contains intr

KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences.

Science.gov (United States)

Ernst, Patrick; Siu, Amy; Weikum, Gerhard

2015-05-14

Biomedical knowledge bases (KB's) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB's based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors. KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from

ICNBME-2011: International Conference on Nanotechnologies and Biomedical Engineering; German-Moldovan Workshop on Novel Nanomaterials for Electronic, Photonic and Biomedical Applications. Proceedings

International Nuclear Information System (INIS)

Tiginyanu, Ion; Sontea, Victor

2011-01-01

This book includes articles which cover a vast range of subjects, such as: nano technologies and nano materials, micro- and nano-objects, nanostructured and highly integrated systems, biophysics, biomedical instrumentation and devices, biomaterials, medical imaging, information technologies for health care, tele medicine, etc.

Biomedical signal and image processing.

Science.gov (United States)

Cerutti, Sergio; Baselli, Giuseppe; Bianchi, Anna; Caiani, Enrico; Contini, Davide; Cubeddu, Rinaldo; Dercole, Fabio; Rienzo, Luca; Liberati, Diego; Mainardi, Luca; Ravazzani, Paolo; Rinaldi, Sergio; Signorini, Maria; Torricelli, Alessandro

2011-01-01

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

Semi-automatic building extraction in informal settlements from high-resolution satellite imagery

Science.gov (United States)

Mayunga, Selassie David

The extraction of man-made features from digital remotely sensed images is considered as an important step underpinning management of human settlements in any country. Man-made features and buildings in particular are required for varieties of applications such as urban planning, creation of geographical information systems (GIS) databases and Urban City models. The traditional man-made feature extraction methods are very expensive in terms of equipment, labour intensive, need well-trained personnel and cannot cope with changing environments, particularly in dense urban settlement areas. This research presents an approach for extracting buildings in dense informal settlement areas using high-resolution satellite imagery. The proposed system uses a novel strategy of extracting building by measuring a single point at the approximate centre of the building. The fine measurement of the building outlines is then effected using a modified snake model. The original snake model on which this framework is based, incorporates an external constraint energy term which is tailored to preserving the convergence properties of the snake model; its use to unstructured objects will negatively affect their actual shapes. The external constrained energy term was removed from the original snake model formulation, thereby, giving ability to cope with high variability of building shapes in informal settlement areas. The proposed building extraction system was tested on two areas, which have different situations. The first area was Tungi in Dar Es Salaam, Tanzania where three sites were tested. This area is characterized by informal settlements, which are illegally formulated within the city boundaries. The second area was Oromocto in New Brunswick, Canada where two sites were tested. Oromocto area is mostly flat and the buildings are constructed using similar materials. Qualitative and quantitative measures were employed to evaluate the accuracy of the results as well as the performance

Sagace: A web-based search engine for biomedical databases in Japan

Directory of Open Access Journals (Sweden)

Morita Mizuki

2012-10-01

Full Text Available Abstract Background In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database. Findings We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data and biological resource banks (such as mouse models of disease and cell lines. With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry. Conclusions Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at http://sagace.nibio.go.jp/en/.

Ethical behaviour of authors in biomedical journalism.

Science.gov (United States)

Bevan, Joan C

2002-03-01

Biomedical journals communicate new information that changes health-care decisions. If authors ignore the fundamental values of honesty and trust, that information becomes flawed, and society or patients may be harmed. By describing two cases of unethical behaviour by authors, and using them as a focus to review acceptable ethics in publication, this article aims to educate readers who have not considered the ethical implications in writing manuscripts for biomedical journals. Two cases of unethical behaviour by authors occurred when the results of new drug trials were reported. They were discovered after publication in a biomedical journal, and in the review process after the submission of a manuscript for publication respectively. In the first case, duplicate publication was identified because the same control data were used, but not acknowledged, in three publications by the same investigators. In the second, ghost writing by a pharmaceutical company writer was suspected because of the atypical presentation of a senior author's work. The editor consulted with the authors of both reports. In the first case, the authors concurred about the duplication, and the editors of the three journals wrote editorials to record the duplicate publications. The second case of ghost writing was unconfirmed by the authors, but the submission was withdrawn, and the article was later published in another journal. These cases draw attention to recently recognized types of scientific misconduct that influence the perception of scientific work. Duplicate publication and ghost writing not only deceive the reader, but may also conceal flawed study design and conflict of interest.

Journal of Biomedical Investigation: Editorial Policies

African Journals Online (AJOL)

Journal of Biomedical Investigation: Editorial Policies. Journal Home ... The focus of the Journal of Biomedical Research is to promote interdisciplinary research across all Biomedical Sciences. It publishes ... Business editor – Sam Meludu.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

Science.gov (United States)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Information analysis of iris biometrics for the needs of cryptology key extraction

Directory of Open Access Journals (Sweden)

Adamović Saša

2013-01-01

Full Text Available The paper presents a rigorous analysis of iris biometric information for the synthesis of an optimized system for the extraction of a high quality cryptology key. Estimations of local entropy and mutual information were identified as segments of the iris most suitable for this purpose. In order to optimize parameters, corresponding wavelets were transformed, in order to obtain the highest possible entropy and mutual information lower in the transformation domain, which set frameworks for the synthesis of systems for the extraction of truly random sequences of iris biometrics, without compromising authentication properties. [Projekat Ministarstva nauke Republike Srbije, br. TR32054 i br. III44006

[Mass media communication of biomedical advances].

Science.gov (United States)

P Salas, Sofía; Beca I, Juan Pablo

2008-10-01

The public dissemination of advances in biomedical research and clinical medicine generates several difficulties and problems. Mass media have the responsibility to report accurately and in a comprehensive way, and physicians and researchers must provide this information in a timely manner and without bias. After reviewing the literature related to this subject and discussing some examples of inadequate information in the Chilean context, the authors suggest the following recommendations: journalists should compare and evaluate the information appropriately before its publication, researchers and journalists should work together, reports should inform clearly about the state of the research and every academic institution should avoid reporting publicly preliminary experiences. If these recommendations are followed, the general public, physicians, researchers and health care institutions will be benefited.

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

Technical editing of research reports in biomedical journals.

Science.gov (United States)

Wager, Elizabeth; Middleton, Philippa

2008-10-08

Most journals try to improve their articles by technical editing processes such as proof-reading, editing to conform to 'house styles', grammatical conventions and checking accuracy of cited references. Despite the considerable resources devoted to technical editing, we do not know whether it improves the accessibility of biomedical research findings or the utility of articles. This is an update of a Cochrane methodology review first published in 2003. To assess the effects of technical editing on research reports in peer-reviewed biomedical journals, and to assess the level of accuracy of references to these reports. We searched The Cochrane Library Issue 2, 2007; MEDLINE (last searched July 2006); EMBASE (last searched June 2007) and checked relevant articles for further references. We also searched the Internet and contacted researchers and experts in the field. Prospective or retrospective comparative studies of technical editing processes applied to original research articles in biomedical journals, as well as studies of reference accuracy. Two review authors independently assessed each study against the selection criteria and assessed the methodological quality of each study. One review author extracted the data, and the second review author repeated this. We located 32 studies addressing technical editing and 66 surveys of reference accuracy. Only three of the studies were randomised controlled trials. A 'package' of largely unspecified editorial processes applied between acceptance and publication was associated with improved readability in two studies and improved reporting quality in another two studies, while another study showed mixed results after stricter editorial policies were introduced. More intensive editorial processes were associated with fewer errors in abstracts and references. Providing instructions to authors was associated with improved reporting of ethics requirements in one study and fewer errors in references in two studies, but no

Information retrieval and terminology extraction in online resources for patients with diabetes.

Science.gov (United States)

Seljan, Sanja; Baretić, Maja; Kucis, Vlasta

2014-06-01

Terminology use, as a mean for information retrieval or document indexing, plays an important role in health literacy. Specific types of users, i.e. patients with diabetes need access to various online resources (on foreign and/or native language) searching for information on self-education of basic diabetic knowledge, on self-care activities regarding importance of dietetic food, medications, physical exercises and on self-management of insulin pumps. Automatic extraction of corpus-based terminology from online texts, manuals or professional papers, can help in building terminology lists or list of "browsing phrases" useful in information retrieval or in document indexing. Specific terminology lists represent an intermediate step between free text search and controlled vocabulary, between user's demands and existing online resources in native and foreign language. The research aiming to detect the role of terminology in online resources, is conducted on English and Croatian manuals and Croatian online texts, and divided into three interrelated parts: i) comparison of professional and popular terminology use ii) evaluation of automatic statistically-based terminology extraction on English and Croatian texts iii) comparison and evaluation of extracted terminology performed on English manual using statistical and hybrid approaches. Extracted terminology candidates are evaluated by comparison with three types of reference lists: list created by professional medical person, list of highly professional vocabulary contained in MeSH and list created by non-medical persons, made as intersection of 15 lists. Results report on use of popular and professional terminology in online diabetes resources, on evaluation of automatically extracted terminology candidates in English and Croatian texts and on comparison of statistical and hybrid extraction methods in English text. Evaluation of automatic and semi-automatic terminology extraction methods is performed by recall

The rolling evolution of biomedical science as an essential tool in modern clinical practice.

Science.gov (United States)

Blann, Andrew

2016-01-01

The British Journal of Biomedical Science is committed to publishing high-quality original research that represents a clear advance in the practice of biomedical science, and reviews that summarise recent advances in the field of biomedical science. The overall aim of the Journal is to provide a platform for the dissemination of new and innovative information on the diagnosis and management of disease that is valuable to the practicing laboratory scientist. The Editorial that follows describes the Journal and provides a perspective of its aims and objectives.

Branding the bio/biomedical engineering degree.

Science.gov (United States)

Voigt, Herbert F

2011-01-01

The future challenges to medical and biological engineering, sometimes referred to as biomedical engineering or simply bioengineering, are many. Some of these are identifiable now and others will emerge from time to time as new technologies are introduced and harnessed. There is a fundamental issue regarding "Branding the bio/biomedical engineering degree" that requires a common understanding of what is meant by a B.S. degree in Biomedical Engineering, Bioengineering, or Biological Engineering. In this paper we address some of the issues involved in branding the Bio/Biomedical Engineering degree, with the aim of clarifying the Bio/Biomedical Engineering brand.

Stemcell Information: SKIP000983 [SKIP Stemcell Database[Archive

Lifescience Database Archive (English)

Full Text Available RT-PCR Rudolf Jaenisch Rudolf Jaenisch Whitehead Institute for Biomedical Researc...h Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical... Research Rudolf Jaenisch Rudolf Jaenisch Information Only Whitehead Institute for Biomedical... Research Whitehead Institute for Biomedical Research ... 24936472 10.1016/j.st

Stemcell Information: SKIP000981 [SKIP Stemcell Database[Archive

Lifescience Database Archive (English)

Full Text Available RT-PCR Rudolf Jaenisch Rudolf Jaenisch Whitehead Institute for Biomedical Researc...h Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical... Research Rudolf Jaenisch Rudolf Jaenisch Information Only Whitehead Institute for Biomedical... Research Whitehead Institute for Biomedical Research ... 24936472 10.1016/j.st

Biomedical nanotechnology.

Science.gov (United States)

Hurst, Sarah J

2011-01-01

This chapter summarizes the roles of nanomaterials in biomedical applications, focusing on those highlighted in this volume. A brief history of nanoscience and technology and a general introduction to the field are presented. Then, the chemical and physical properties of nanostructures that make them ideal for use in biomedical applications are highlighted. Examples of common applications, including sensing, imaging, and therapeutics, are given. Finally, the challenges associated with translating this field from the research laboratory to the clinic setting, in terms of the larger societal implications, are discussed.

EVALUATION OF BIOMEDICAL WASTE MANAGEMENT PRACTICES IN MULTI-SPECIALITY TERTIARY HOSPITAL

Directory of Open Access Journals (Sweden)

Shalini Srivastav

2010-06-01

Full Text Available Background: Biomedical Waste (BMW, collection and proper disposal has become a significant concern for both the medical and the general community The scientific “Hospital waste Management “is of vital importance as its improper management poses risks to the health care workers ,waste handlers patients, community in general and largely the environment. Objectives: (i To assess current practices of Bio-medical Waste management including generation, collection, transportation storage, treatment and disposal technologies in tertiary health care center. (ii To assess health andsafetypracticesfor the health care personnel involved in Bio-Medical waste Management. Materials and Methods: Waste management practices in tertiary care-centre was studied during May 2010 June 2010. The information/data regarding Bio-Medical Waste Management practices and safety was collected by way of semi structured interview, proforma being the one used for WASTE AUDITING QUESTIONNAIRE. The information collected was verified by personal observations of waste management practices in each ward of hospital. Results : SRMS-IMS generates 1. 25Kgs waste per bed per day and maximum waste is generated in wards. The institute has got separate color coded bins in each ward for collection of waste but segregation practices needs to be more refined. The safety measures taken by health care workers was not satisfactory it was not due to unavailability of Personal protective measures but because of un-awareness of health hazards which may occur due to improper waste management practices. Thus it is concluded that there should be strict implementation of a waste management policy set up in the institute, training and motivation must be given paramount importance to meet the current needs and standard of bio-medical waste management.

Adolescent Self-Consent for Biomedical Human Immunodeficiency Virus Prevention Research.

Science.gov (United States)

Gilbert, Amy Lewis; Knopf, Amelia S; Fortenberry, J Dennis; Hosek, Sybil G; Kapogiannis, Bill G; Zimet, Gregory D

2015-07-01

The Adolescent Medicine Trials Network Protocol 113 (ATN113) is an open-label, multisite demonstration project and Phase II safety study of human immunodeficiency virus (HIV) preexposure prophylaxis with 15- to 17-year-old young men who have sex with men that requires adolescent consent for participation. The purpose of this study was to examine factors related to the process by which Institutional Review Boards (IRBs) and researchers made decisions regarding whether to approve and implement ATN113 so as to inform future biomedical HIV prevention research with high-risk adolescent populations. Participants included 17 researchers at 13 sites in 12 states considering ATN113 implementation. Qualitative descriptive methods were used. Data sources included interviews and documents generated during the initiation process. A common process for initiating ATN113 emerged, and informants described how they identified and addressed practical, ethical, and legal challenges that arose. Informants described the process as responding to the protocol, preparing for IRB submission, abstaining from or proceeding with submission, responding to IRB concerns, and reacting to the outcomes. A complex array of factors impacting approval and implementation were identified, and ATN113 was ultimately implemented in seven of 13 sites. Informants also reflected on lessons learned that may help inform future biomedical HIV prevention research with high-risk adolescent populations. The results illustrate factors for consideration in determining whether to implement such trials, demonstrate that such protocols have the potential to be approved, and highlight a need for clearer standards regarding biomedical HIV prevention research with high-risk adolescent populations. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Biomedical applications of polymers

CERN Document Server

Gebelein, C G

1991-01-01

The biomedical applications of polymers span an extremely wide spectrum of uses, including artificial organs, skin and soft tissue replacements, orthopaedic applications, dental applications, and controlled release of medications. No single, short review can possibly cover all these items in detail, and dozens of books andhundreds of reviews exist on biomedical polymers. Only a few relatively recent examples will be cited here;additional reviews are listed under most of the major topics in this book. We will consider each of the majorclassifications of biomedical polymers to some extent, inclu

Biomedical Engineering Desk Reference

CERN Document Server

Ratner, Buddy D; Schoen, Frederick J; Lemons, Jack E; Dyro, Joseph; Martinsen, Orjan G; Kyle, Richard; Preim, Bernhard; Bartz, Dirk; Grimnes, Sverre; Vallero, Daniel; Semmlow, John; Murray, W Bosseau; Perez, Reinaldo; Bankman, Isaac; Dunn, Stanley; Ikada, Yoshito; Moghe, Prabhas V; Constantinides, Alkis

2009-01-01

A one-stop Desk Reference, for Biomedical Engineers involved in the ever expanding and very fast moving area; this is a book that will not gather dust on the shelf. It brings together the essential professional reference content from leading international contributors in the biomedical engineering field. Material covers a broad range of topics including: Biomechanics and Biomaterials; Tissue Engineering; and Biosignal Processing* A hard-working desk reference providing all the essential material needed by biomedical and clinical engineers on a day-to-day basis * Fundamentals, key techniques,

A scoping review of competencies for scientific editors of biomedical journals.

Science.gov (United States)

Galipeau, James; Barbour, Virginia; Baskin, Patricia; Bell-Syer, Sally; Cobey, Kelly; Cumpston, Miranda; Deeks, Jon; Garner, Paul; MacLehose, Harriet; Shamseer, Larissa; Straus, Sharon; Tugwell, Peter; Wager, Elizabeth; Winker, Margaret; Moher, David

2016-02-02

Biomedical journals are the main route for disseminating the results of health-related research. Despite this, their editors operate largely without formal training or certification. To our knowledge, no body of literature systematically identifying core competencies for scientific editors of biomedical journals exists. Therefore, we aimed to conduct a scoping review to determine what is known on the competency requirements for scientific editors of biomedical journals. We searched the MEDLINE®, Cochrane Library, Embase®, CINAHL, PsycINFO, and ERIC databases (from inception to November 2014) and conducted a grey literature search for research and non-research articles with competency-related statements (i.e. competencies, knowledge, skills, behaviors, and tasks) pertaining to the role of scientific editors of peer-reviewed health-related journals. We also conducted an environmental scan, searched the results of a previous environmental scan, and searched the websites of existing networks, major biomedical journal publishers, and organizations that offer resources for editors. A total of 225 full-text publications were included, 25 of which were research articles. We extracted a total of 1,566 statements possibly related to core competencies for scientific editors of biomedical journals from these publications. We then collated overlapping or duplicate statements which produced a list of 203 unique statements. Finally, we grouped these statements into seven emergent themes: (1) dealing with authors, (2) dealing with peer reviewers, (3) journal publishing, (4) journal promotion, (5) editing, (6) ethics and integrity, and (7) qualities and characteristics of editors. To our knowledge, this scoping review is the first attempt to systematically identify possible competencies of editors. Limitations are that (1) we may not have captured all aspects of a biomedical editor's work in our searches, (2) removing redundant and overlapping items may have led to the

[Extraction of buildings three-dimensional information from high-resolution satellite imagery based on Barista software].

Science.gov (United States)

Zhang, Pei-feng; Hu, Yuan-man; He, Hong-shi

2010-05-01

The demand for accurate and up-to-date spatial information of urban buildings is becoming more and more important for urban planning, environmental protection, and other vocations. Today's commercial high-resolution satellite imagery offers the potential to extract the three-dimensional information of urban buildings. This paper extracted the three-dimensional information of urban buildings from QuickBird imagery, and validated the precision of the extraction based on Barista software. It was shown that the extraction of three-dimensional information of the buildings from high-resolution satellite imagery based on Barista software had the advantages of low professional level demand, powerful universality, simple operation, and high precision. One pixel level of point positioning and height determination accuracy could be achieved if the digital elevation model (DEM) and sensor orientation model had higher precision and the off-Nadir View Angle was relatively perfect.

Biomedical research applications

International Nuclear Information System (INIS)

Anon.

1982-01-01

The biomedical research Panel believes that the Calutron facility at Oak Ridge is a national and international resource of immense scientific value and of fundamental importance to continued biomedical research. This resource is essential to the development of new isotope uses in biology and medicine. It should therefore be nurtured by adequate support and operated in a way that optimizes its services to the scientific and technological community. The Panel sees a continuing need for a reliable supply of a wide variety of enriched stable isotopes. The past and present utilization of stable isotopes in biomedical research is documented in Appendix 7. Future requirements for stable isotopes are impossible to document, however, because of the unpredictability of research itself. Nonetheless we expect the demand for isotopes to increase in parallel with the continuing expansion of biomedical research as a whole. There are a number of promising research projects at the present time, and these are expected to lead to an increase in production requirements. The Panel also believes that a high degree of priority should be given to replacing the supplies of the 65 isotopes (out of the 224 previously available enriched isotopes) no longer available from ORNL

Biomedical informatics and the convergence of Nano-Bio-Info-Cogno (NBIC) technologies.

Science.gov (United States)

Martin-Sanchez, F; Maojo, V

2009-01-01

To analyze the role that biomedical informatics could play in the application of the NBIC Converging Technologies in the medical field and raise awareness of these new areas throughout the Biomedical Informatics community. Review of the literature and analysis of the reference documents in this domain from the biomedical informatics perspective. Detailing existing developments showing that partial convergence of technologies have already yielded relevant results in biomedicine (such as bioinformatics or biochips). Input from current projects in which the authors are involved is also used. Information processing is a key issue in enabling the convergence of NBIC technologies. Researchers in biomedical informatics are in a privileged position to participate and actively develop this new scientific direction. The experience of biomedical informaticians in five decades of research in the medical area and their involvement in the completion of the Human and other genome projects will help them participate in a similar role for the development of applications of converging technologies -particularly in nanomedicine. The proposed convergence will bring bridges between traditional disciplines. Particular attention should be placed on the ethical, legal, and social issues raised by the NBIC convergence. These technologies provide new directions for research and education in Biomedical Informatics placing a greater emphasis in multidisciplinary approaches.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

Science.gov (United States)

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Characterizing semantic mappings adaptation via biomedical KOS evolution: a case study investigating SNOMED CT and ICD.

Science.gov (United States)

Dos Reis, Julio Cesar; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2013-01-01

Mappings established between Knowledge Organization Systems (KOS) increase semantic interoperability between biomedical information systems. However, biomedical knowledge is highly dynamic and changes affecting KOS entities can potentially invalidate part or the totality of existing mappings. Understanding how mappings evolve and what the impacts of KOS evolution on mappings are is therefore crucial for the definition of an automatic approach to maintain mappings valid and up-to-date over time. In this article, we study variations of a specific KOS complex change (split) for two biomedical KOS (SNOMED CT and ICD-9-CM) through a rigorous method of investigation for identifying and refining complex changes, and for selecting representative cases. We empirically analyze and explain their influence on the evolution of associated mappings. Results point out the importance of considering various dimensions of the information described in KOS, like the semantic structure of concepts, the set of relevant information used to define the mappings and the change operations interfering with this set of information.

Biomedical signal analysis

CERN Document Server

Rangayyan, Rangaraj M

2015-01-01

The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications. 800 mathematical expressions and equations. Practical questions, problems and laboratory exercises. Includes fractals and chaos theory with biomedical applications.

A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories.

Science.gov (United States)

Yang, Wei; Ai, Tinghua; Lu, Wei

2018-04-19

Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS) traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction) by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality.

A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories

Directory of Open Access Journals (Sweden)

Wei Yang

2018-04-01

Full Text Available Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT. First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality.

Biomedical Information Extraction: Mining Disease Associated Genes from Literature

Science.gov (United States)

Huang, Zhong

2014-01-01

Disease associated gene discovery is a critical step to realize the future of personalized medicine. However empirical and clinical validation of disease associated genes are time consuming and expensive. In silico discovery of disease associated genes from literature is therefore becoming the first essential step for biomarker discovery to…

ICNBME-2013: 2. international conference on nanotechnologies and biomedical engineering; German-Moldovan workshop on novel nanomaterials for electronic, photonic and biomedical applications. Proceedings

International Nuclear Information System (INIS)

Tiginyanu, Ion; Sontea, Victor

2013-01-01

This book includes articles which cover a vast range of subjects, such as: nano technologies and nano materials, micro- and nano-objects, nanostructured and highly integrated systems, biophysics, biomedical instrumentation and devices, biomaterials, medical imaging, information technologies for health care, tele medicine, etc.

Stemcell Information: SKIP000697 [SKIP Stemcell Database[Archive

Lifescience Database Archive (English)

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Stemcell Information: SKIP000694 [SKIP Stemcell Database[Archive

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Stemcell Information: SKIP000695 [SKIP Stemcell Database[Archive

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A cascade of classifiers for extracting medication information from discharge summaries

Directory of Open Access Journals (Sweden)

Halgrim Scott

2011-07-01

Full Text Available Abstract Background Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task. Methods We present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events. Results The system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists. Conclusions This study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.

Exploring and linking biomedical resources through multidimensional semantic spaces.

Science.gov (United States)

Berlanga, Rafael; Jiménez-Ruiz, Ernesto; Nebot, Victoria

2012-01-25

The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for

Research evaluation support services in biomedical libraries.

Science.gov (United States)

Gutzman, Karen Elizabeth; Bales, Michael E; Belter, Christopher W; Chambers, Thane; Chan, Liza; Holmes, Kristi L; Lu, Ya-Ling; Palmer, Lisa A; Reznik-Zellen, Rebecca C; Sarli, Cathy C; Suiter, Amy M; Wheeler, Terrie R

2018-01-01

The paper provides a review of current practices related to evaluation support services reported by seven biomedical and research libraries. A group of seven libraries from the United States and Canada described their experiences with establishing evaluation support services at their libraries. A questionnaire was distributed among the libraries to elicit information as to program development, service and staffing models, campus partnerships, training, products such as tools and reports, and resources used for evaluation support services. The libraries also reported interesting projects, lessons learned, and future plans. The seven libraries profiled in this paper report a variety of service models in providing evaluation support services to meet the needs of campus stakeholders. The service models range from research center cores, partnerships with research groups, and library programs with staff dedicated to evaluation support services. A variety of products and services were described such as an automated tool to develop rank-based metrics, consultation on appropriate metrics to use for evaluation, customized publication and citation reports, resource guides, classes and training, and others. Implementing these services has allowed the libraries to expand their roles on campus and to contribute more directly to the research missions of their institutions. Libraries can leverage a variety of evaluation support services as an opportunity to successfully meet an array of challenges confronting the biomedical research community, including robust efforts to report and demonstrate tangible and meaningful outcomes of biomedical research and clinical care. These services represent a transformative direction that can be emulated by other biomedical and research libraries.

Enrolment and Retention of African Women in Biomedical Research ...

African Journals Online (AJOL)

Relevant biomedical research literatures on Human Research Participants from Scirus, Pubmed and Medline computerized search were critically evaluated and highlighted. Information was also obtained from research ethics training as well as texts and journals in the medical libraries of the research ethics departments of ...

Stemcell Information: SKIP000979 [SKIP Stemcell Database[Archive

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Stemcell Information: SKIP000977 [SKIP Stemcell Database[Archive

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Full Text Available Jaenisch Rudolf Jaenisch Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Rese...arch Whitehead Institute for Biomedical Research Whitehead Institute for Biomedical Research Rudolf Jaenisch... Rudolf Jaenisch Information Only Whitehead Institute for Biomedical Research Whi...tehead Institute for Biomedical Research ... 24936472 10.1016/j.stemcr.2014.03.014 Genetic and chemical cor

Stemcell Information: SKIP000980 [SKIP Stemcell Database[Archive

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Stemcell Information: SKIP000978 [SKIP Stemcell Database[Archive

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New software developments for quality mesh generation and optimization from biomedical imaging data.

Science.gov (United States)

Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Xu, Ming; Hoshijima, Masahiko

2014-01-01

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Rapid automatic keyword extraction for information retrieval and analysis

Science.gov (United States)

Rose, Stuart J [Richland, WA; Cowley,; E, Wendy [Richland, WA; Crow, Vernon L [Richland, WA; Cramer, Nicholas O [Richland, WA

2012-03-06

Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

Customization of biomedical terminologies.

Science.gov (United States)

Homo, Julien; Dupuch, Laëtitia; Benbrahim, Allel; Grabar, Natalia; Dupuch, Marie

2012-01-01

Within the biomedical area over one hundred terminologies exist and are merged in the Unified Medical Language System Metathesaurus, which gives over 1 million concepts. When such huge terminological resources are available, the users must deal with them and specifically they must deal with irrelevant parts of these terminologies. We propose to exploit seed terms and semantic distance algorithms in order to customize the terminologies and to limit within them a semantically homogeneous space. An evaluation performed by a medical expert indicates that the proposed approach is relevant for the customization of terminologies and that the extracted terms are mostly relevant to the seeds. It also indicates that different algorithms provide with similar or identical results within a given terminology. The difference is due to the terminologies exploited. A special attention must be paid to the definition of optimal association between the semantic similarity algorithms and the thresholds specific to a given terminology.

Ultra low-power biomedical signal processing: An analog wavelet filter approach for pacemakers

OpenAIRE

Pavlík Haddad, S.A.

2006-01-01

The purpose of this thesis is to describe novel signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly non-stationary. The main difficulty in dealing with biomedical signal processing is that the information of interest is often a combination of features that are well localized temporally (e.g., spikes) and other...

Rotation Covariant Image Processing for Biomedical Applications

Directory of Open Access Journals (Sweden)

Henrik Skibbe

2013-01-01

Full Text Available With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

A method for automatically extracting infectious disease-related primers and probes from the literature

Directory of Open Access Journals (Sweden)

Pérez-Rey David

2010-08-01

Full Text Available Abstract Background Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1 convert each document into a tree of paper sections, (2 detect the candidate sequences using a set of finite state machine-based recognizers, (3 refine problem sequences using a rule-based expert system, and (4 annotate the extracted sequences with their related organism/gene information. Results We tested our approach using a test set composed of 297 manuscripts. The extracted sequences and their organism/gene annotations were manually evaluated by a panel of molecular biologists. The results of the evaluation show that our approach is suitable for automatically extracting DNA sequences, achieving precision/recall rates of 97.98% and 95.77%, respectively. In addition, 76.66% of the detected sequences were correctly annotated with their organism name. The system also provided correct gene-related information for 46.18% of the sequences assigned a correct organism name. Conclusions We believe that the proposed method can facilitate routine tasks for biomedical researchers using molecular methods to diagnose and prescribe different infectious diseases. In addition, the proposed method can be expanded to detect and extract other biological sequences from the literature. The extracted information can also be used to readily update available primer/probe databases or to create new databases from scratch.

The ethics of biomedical big data

CERN Document Server

Mittelstadt, Brent Daniel

2016-01-01

This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understan...

Biomedical Engineering: A Compendium of Research Training Programs.

Science.gov (United States)

National Inst. of General Medical Sciences (NIH), Bethesda, MD.

This document was prepared to provide a comprehensive view of the programs in biomedical engineering in existence in 1969. These programs are supported by the National Institute of General Medical Sciences and are located at 18 universities. This compendium provides information as to the intent and content of these programs from data provided by…

Bayes' theorem: A paradigm research tool in biomedical sciences ...

African Journals Online (AJOL)

One of the most interesting applications of the results of probability theory involves estimating unknown probability and making decisions on the basis of new (sample) information. Biomedical scientists often use the Bayesian decision theory for the purposes of computing diagnostic values such as sensitivity and specificity ...

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

Science.gov (United States)

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

Bio-medical X-ray imaging with spectroscopic pixel detectors

CERN Document Server

Butler, A P H; Tipples, R; Cook, N; Watts, R; Meyer, J; Bell, A J; Melzer, T R; Butler, P H

2008-01-01

The aim of this study is to review the clinical potential of spectroscopic X-ray detectors and to undertake a feasibility study using a novel detector in a clinical hospital setting. Detectors currently in development, such as Medipix-3, will have multiple energy thresholds allowing for routine use of spectroscopic bio-medical imaging. We have coined the term MARS (Medipix All Resolution System) for bio-medical images that provide spatial, temporal, and energy information. The full clinical significance of spectroscopic X-ray imaging is difficult to predict but insights can be gained by examining both image reconstruction artifacts and the current uses of dual-energy techniques. This paper reviews the known uses of energy information in vascular imaging and mammography, clinically important fields. It then presents initial results from using Medipix-2, to image human tissues within a clinical radiology department. Detectors currently in development, such as Medipix-3, will have multiple energy thresholds allo...

Biomedical applications engineering tasks

Science.gov (United States)

Laenger, C. J., Sr.

1976-01-01

The engineering tasks performed in response to needs articulated by clinicians are described. Initial contacts were made with these clinician-technology requestors by the Southwest Research Institute NASA Biomedical Applications Team. The basic purpose of the program was to effectively transfer aerospace technology into functional hardware to solve real biomedical problems.

Robust Vehicle and Traffic Information Extraction for Highway Surveillance

Directory of Open Access Journals (Sweden)

Yeh Chia-Hung

2005-01-01

Full Text Available A robust vision-based traffic monitoring system for vehicle and traffic information extraction is developed in this research. It is challenging to maintain detection robustness at all time for a highway surveillance system. There are three major problems in detecting and tracking a vehicle: (1 the moving cast shadow effect, (2 the occlusion effect, and (3 nighttime detection. For moving cast shadow elimination, a 2D joint vehicle-shadow model is employed. For occlusion detection, a multiple-camera system is used to detect occlusion so as to extract the exact location of each vehicle. For vehicle nighttime detection, a rear-view monitoring technique is proposed to maintain tracking and detection accuracy. Furthermore, we propose a method to improve the accuracy of background extraction, which usually serves as the first step in any vehicle detection processing. Experimental results are given to demonstrate that the proposed techniques are effective and efficient for vision-based highway surveillance.

The development of large-scale de-identified biomedical databases in the age of genomics-principles and challenges.

Science.gov (United States)

Dankar, Fida K; Ptitsyn, Andrey; Dankar, Samar K

2018-04-10

Contemporary biomedical databases include a wide range of information types from various observational and instrumental sources. Among the most important features that unite biomedical databases across the field are high volume of information and high potential to cause damage through data corruption, loss of performance, and loss of patient privacy. Thus, issues of data governance and privacy protection are essential for the construction of data depositories for biomedical research and healthcare. In this paper, we discuss various challenges of data governance in the context of population genome projects. The various challenges along with best practices and current research efforts are discussed through the steps of data collection, storage, sharing, analysis, and knowledge dissemination.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

Science.gov (United States)

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust

[Master course in biomedical engineering].

Science.gov (United States)

Jobbágy, Akos; Benyó, Zoltán; Monos, Emil

2009-11-22

The Bologna Declaration aims at harmonizing the European higher education structure. In accordance with the Declaration, biomedical engineering will be offered as a master (MSc) course also in Hungary, from year 2009. Since 1995 biomedical engineering course has been held in cooperation of three universities: Semmelweis University, Budapest Veterinary University, and Budapest University of Technology and Economics. One of the latter's faculties, Faculty of Electrical Engineering and Informatics, has been responsible for the course. Students could start their biomedical engineering studies - usually in parallel with their first degree course - after they collected at least 180 ECTS credits. Consequently, the biomedical engineering course could have been considered as a master course even before the Bologna Declaration. Students had to collect 130 ECTS credits during the six-semester course. This is equivalent to four-semester full-time studies, because during the first three semesters the curriculum required to gain only one third of the usual ECTS credits. The paper gives a survey on the new biomedical engineering master course, briefly summing up also the subjects in the curriculum.

Support patient search on pathology reports with interactive online learning based data extraction.

Science.gov (United States)

Zheng, Shuai; Lu, James J; Appin, Christina; Brat, Daniel; Wang, Fusheng

2015-01-01

Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Extracting data from pathology reports could enable

People with low back pain perceive needs for non-biomedical services in workplace, financial, social and household domains: a systematic review.

Science.gov (United States)

Chou, Louisa; Cicuttini, Flavia M; Urquhart, Donna M; Anthony, Shane N; Sullivan, Kaye; Seneviwickrama, Maheeka; Briggs, Andrew M; Wluka, Anita E

2018-04-01

What needs of non-biomedical services are perceived by people with low back pain? Systematic review of qualitative and quantitative studies examining perceived needs of non-biomedical services for low back pain, identified through searching of MEDLINE, EMBASE, CINAHL and PsycINFO (1990 to 2016). Adults with low back pain of any duration. Descriptive data regarding study design and methodology were extracted. The preferences, expectations and satisfaction with non-biomedical services reported by people with low back pain were identified and categorised within areas of perceived need. Twenty studies (19 qualitative and one quantitative) involving 522 unique participants (total pool of 590) were included in this systematic review. Four areas emerged. Workplace: people with low back pain experience pressure to return to work despite difficulties with the demands of their occupation. They want their employers to be informed about low back pain and they desire workplace accommodations. Financial: people with low back pain want financial support, but have concerns about the inefficiencies of compensation systems and the stigma associated with financial remuneration. Social: people with low back pain report feeling disconnected from social networks and want back-specific social support. Household: people with low back pain report difficulties with household duties; however, there are few data regarding their need for auxiliary devices and domestic help. People with low back pain identified work place, financial and social pressures, and difficulties with household duties as areas of need beyond their healthcare requirements that affect their ability to comply with management of their condition. Consideration of such needs may inform physiotherapists, the wider health system, social networks and the workplace to provide more relevant and effective services. [Chou L, Cicuttini FM, Urquhart DM, Anthony SN, Sullivan K, Seneviwickrama M, Briggs AM, Wluka AE (2018) People with

Biomedical photoacoustics: fundamentals, instrumentation and perspectives on nanomedicine.

Science.gov (United States)

Zou, Chunpeng; Wu, Beibei; Dong, Yanyan; Song, Zhangwei; Zhao, Yaping; Ni, Xianwei; Yang, Yan; Liu, Zhe

Photoacoustic imaging (PAI) is an integrated biomedical imaging modality which combines the advantages of acoustic deep penetration and optical high sensitivity. It can provide functional and structural images with satisfactory resolution and contrast which could provide abundant pathological information for disease-oriented diagnosis. Therefore, it has found vast applications so far and become a powerful tool of precision nanomedicine. However, the investigation of PAI-based imaging nanomaterials is still in its infancy. This perspective article aims to summarize the developments in photoacoustic technologies and instrumentations in the past years, and more importantly, present a bright outlook for advanced PAI-based imaging nanomaterials as well as their emerging biomedical applications in nanomedicine. Current challenges and bottleneck issues have also been discussed and elucidated in this article to bring them to the attention of the readership.

PubMed-based quantitative analysis of biomedical publications in the SAARC countries: 1985-2009.

Science.gov (United States)

Azim Majumder, Md Anwarul; Shaban, Sami F; Rahman, Sayeeda; Rahman, Nuzhat; Ahmed, Moslehuddin; Bin Abdulrahman, Khalid A; Islam, Ziauddin

2012-09-01

To conduct a geographical analysis of biomedical publications from the South Asian Association for Regional Cooperation (SAARC) countries over the past 25 years (1985-2009) using the PubMed database. A qualitative study. Web-based search during September 2010. A data extraction program, developed by one of the authors (SFS), was used to extract the raw publication counts from the downloaded PubMed data. A search of PubMed was performed for all journals indexed by selecting the advanced search option and entering the country name in the 'affiliation' field. The publications were normalized by total population, adult illiteracy rate, gross domestic product (GDP), secondary school enrollment ratio and Internet usage rate. The number of PubMed-listed papers published by the SAARC countries over the last 25 years totalled 141,783, which is 1.1% of the total papers indexed by PubMed in the same period. India alone produced 90.5% of total publications generated by SAARC countries. The average number of papers published per year from 1985 to 2009 was 5671 and number of publication increased approximately 242-fold. Normalizing by the population (per million) and GDP (per billion), India (133, 27.6%) and Nepal (323, 37.3%) had the highest publications respectively. There was a marked imbalance among the SAARC countries in terms of biomedical research and publication. Because of huge population and the high disease burden, biomedical research and publication output should receive special attention to formulate health policies, re-orient medical education curricula, and alleviate diseases and poverty.

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

Science.gov (United States)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Biomedical engineering frontier research and converging technologies

CERN Document Server

Jun, Ho-Wook; Shin, Jennifer; Lee, SangHoon

2016-01-01

This book provides readers with an integrative overview of the latest research and developments in the broad field of biomedical engineering. Each of the chapters offers a timely review written by leading biomedical engineers and aims at showing how the convergence of scientific and engineering fields with medicine has created a new basis for practically solving problems concerning human health, wellbeing and disease. While some of the latest frontiers of biomedicine, such as neuroscience and regenerative medicine, are becoming increasingly dependent on new ideas and tools from other disciplines, the paradigm shift caused by technological innovations in the fields of information science, nanotechnology, and robotics is opening new opportunities in healthcare, besides dramatically changing the ways we actually practice science. At the same time, a new generation of engineers, fluent in many different scientific “languages,” is creating entirely new fields of research that approach the “old” questions f...

Radiochemicals in biomedical research

International Nuclear Information System (INIS)

Evans, E.A.; Oldham, K.G.

1988-01-01

This volume describes the role of radiochemicals in biomedical research, as tracers in the development of new drugs, their interaction and function with receptor proteins, with the kinetics of binding of hormone - receptor interactions, and their use in cancer research and clinical oncology. The book also aims to identify future trends in this research, the main objective of which is to provide information leading to improvements in the quality of life, and to give readers a basic understanding of the development of new drugs, how they function in relation to receptor proteins and lead to a better understanding of the diagnosis and treatment of cancers. (author)

Extracting Social Networks and Contact Information From Email and the Web

National Research Council Canada - National Science Library

Culotta, Aron; Bekkerman, Ron; McCallum, Andrew

2005-01-01

...-suited for such information extraction tasks. By recursively calling itself on new people discovered on the Web, the system builds a social network with multiple degrees of separation from the user...

Noise-assisted data processing with empirical mode decomposition in biomedical signals.

Science.gov (United States)

Karagiannis, Alexandros; Constantinou, Philip

2011-01-01

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

Biomedical Engineering in Modern Society

Science.gov (United States)

Attinger, E. O.

1971-01-01

Considers definition of biomedical engineering (BME) and how biomedical engineers should be trained. State of the art descriptions of BME and BME education are followed by a brief look at the future of BME. (TS)

Automated Extraction Of Associations Between Methylated Genes and Diseases From Biomedical Literature

KAUST Repository

Bin Res, Arwa A.

2012-01-01

. Based on this model, we developed a tool that automates extraction of associations between methylated genes and diseases from electronic text. Our study contributed an efficient method for extracting specific types of associations from free text

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts

Directory of Open Access Journals (Sweden)

Spackman K

2005-04-01

Full Text Available Abstract Background Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. Results Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall, with high efficiency in the use of seed pairs. Conclusion The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.

SciRide Finder: a citation-based paradigm in biomedical literature search.

Science.gov (United States)

Volanakis, Adam; Krawczyk, Konrad

2018-04-18

There are more than 26 million peer-reviewed biomedical research items according to Medline/PubMed. This breadth of information is indicative of the progress in biomedical sciences on one hand, but an overload for scientists performing literature searches on the other. A major portion of scientific literature search is to find statements, numbers and protocols that can be cited to build an evidence-based narrative for a new manuscript. Because science builds on prior knowledge, such information has likely been written out and cited in an older manuscript. Thus, Cited Statements, pieces of text from scientific literature supported by citing other peer-reviewed publications, carry significant amount of condensed information on prior art. Based on this principle, we propose a literature search service, SciRide Finder (finder.sciride.org), which constrains the search corpus to such Cited Statements only. We demonstrate that Cited Statements can carry different information to this found in titles/abstracts and full text, giving access to alternative literature search results than traditional search engines. We further show how presenting search results as a list of Cited Statements allows researchers to easily find information to build an evidence-based narrative for their own manuscripts.

Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

Directory of Open Access Journals (Sweden)

Peng Shao

2014-08-01

Full Text Available The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.

Biochemical imaging of tissues by SIMS for biomedical applications

International Nuclear Information System (INIS)

Lee, Tae Geol; Park, Ji-Won; Shon, Hyun Kyong; Moon, Dae Won; Choi, Won Woo; Li, Kapsok; Chung, Jin Ho

2008-01-01

With the development of optimal surface cleaning techniques by cluster ion beam sputtering, certain applications of SIMS for analyzing cells and tissues have been actively investigated. For this report, we collaborated with bio-medical scientists to study bio-SIMS analyses of skin and cancer tissues for biomedical diagnostics. We pay close attention to the setting up of a routine procedure for preparing tissue specimens and treating the surface before obtaining the bio-SIMS data. Bio-SIMS was used to study two biosystems, skin tissues for understanding the effects of photoaging and colon cancer tissues for insight into the development of new cancer diagnostics for cancer. Time-of-flight SIMS imaging measurements were taken after surface cleaning with cluster ion bombardment by Bi n or C 60 under varying conditions. The imaging capability of bio-SIMS with a spatial resolution of a few microns combined with principal component analysis reveal biologically meaningful information, but the lack of high molecular weight peaks even with cluster ion bombardment was a problem. This, among other problems, shows that discourse with biologists and medical doctors are critical to glean any meaningful information from SIMS mass spectrometric and imaging data. For SIMS to be accepted as a routine, daily analysis tool in biomedical laboratories, various practical sample handling methodology such as surface matrix treatment, including nano-metal particles and metal coating, in addition to cluster sputtering, should be studied

Electrophysiology for biomedical engineering students: a practical and theoretical course in animal electrocorticography.

Science.gov (United States)

Albarracín, Ana L; Farfán, Fernando D; Coletti, Marcos A; Teruya, Pablo Y; Felice, Carmelo J

2016-09-01

The major challenge in laboratory teaching is the application of abstract concepts in simple and direct practical lessons. However, students rarely have the opportunity to participate in a laboratory that combines practical learning with a realistic research experience. In the Biomedical Engineering career, we offer short and optional courses to complement studies for students as they initiate their Graduation Project. The objective of these theoretical and practical courses is to introduce students to the topics of their projects. The present work describes an experience in electrophysiology to teach undergraduate students how to extract cortical information using electrocorticographic techniques. Students actively participate in some parts of the experience and then process and analyze the data obtained with different signal processing tools. In postlaboratory evaluations, students described the course as an exceptional opportunity for students interested in following a postgraduate science program and fully appreciated their contents. Copyright © 2016 The American Physiological Society.

The biomedical discourse relation bank

Directory of Open Access Journals (Sweden)

Joshi Aravind

2011-05-01

Full Text Available Abstract Background Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical discourse processing. However, little effort has been made to develop such an annotated resource. Results We have developed the Biomedical Discourse Relation Bank (BioDRB, in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB, which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse connectives show the connective itself as a highly reliable indicator for coarse sense classification (accuracy 90.9% and F1 score 0.89. These results are comparable to results obtained with the same classifier on the PDTB data. With more refined sense classification, there is degradation in performance (accuracy 69.2% and F1 score 0.28, mainly due to sparsity in the data. The size of the corpus was found to be sufficient for identifying the sense of explicit connectives, with classifier performance stabilizing at about 1900 training instances. Finally, the classifier performs poorly when trained on PDTB and tested on BioDRB (accuracy 54.5% and F1 score 0.57. Conclusion Our work shows that discourse relations can be reliably annotated in biomedical text. Coarse sense disambiguation of explicit connectives can be done with high reliability by using just the connective as a feature, but more refined sense classification requires either richer features or more

Biomedical Science Technologists in Lagos Universities: Meeting ...

African Journals Online (AJOL)

Biomedical Science Technologists in Lagos Universities: Meeting Modern Standards ... like to see in biomedical science in Nigeria; 5) their knowledge of ten state-of-the-arts ... KEY WORDS: biomedical science, state-of-the-arts, technical staff ...

Resource for the Development of Biomedical Accelerator Mass Spectrometry (AMS)

Energy Technology Data Exchange (ETDEWEB)

Turteltaub, K. W. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Bench, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Buchholz, B. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Enright, H. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kulp, K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); McCartt, A. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Malfatti, M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ognibene, T. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Loots, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stewart, B. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-04-08

The NIH Research Resource for Biomedical AMS was originally funded at Lawrence Livermore National Laboratory in 1999 to develop and apply the technology of accelerator mass spectrometry (AMS) in broad- based biomedical research. The Resource’s niche is to fill needs for ultra high sensitivity quantitation when isotope-labeled agents are used. The Research Resource’s Technology Research and Development (TR&D) efforts will focus on the needs of the biomedical research community in the context of seven Driving Biomedical Projects (DBPs) that will drive the Center’s technical capabilities through three core TR&Ds. We will expand our present capabilities by developing a fully integrated HPLC AMS to increase our capabilities for metabolic measurements, we will develop methods to understand cellular processes and we will develop and validate methods for the application of AMS in human studies, which is a growing area of demand by collaborators and service users. In addition, we will continue to support new and ongoing collaborative and service projects that require the capabilities of the Resource. The Center will continue to train researchers in the use of the AMS capabilities being developed, and the results of all efforts will be widely disseminated to advance progress in biomedical research. Towards these goals, our specific aims are to:1.) Increase the value and information content of AMS measurements by combining molecular speciation with quantitation of defined macromolecular isolates. Specifically, develop and validate methods for macromolecule labeling, characterization and quantitation.2.) Develop and validate methods and strategies to enable AMS to become more broadly used in human studies. Specifically, demonstrate robust methods for conducting pharmacokinetic/pharmacodynamics studies in humans and model systems.3.) Increase the accessibility of AMS to the Biomedical research community and the throughput of AMS through direct coupling to separatory

Resource for the Development of Biomedical Accelerator Mass Spectrometry (AMS)

Energy Technology Data Exchange (ETDEWEB)

Tuerteltaub, K. W. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Bench, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Buchholz, B. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Enright, H. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kulp, K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Loots, G. G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); McCartt, A. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Malfatti, M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ognibene, T. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stewart, B. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-03-21

The NIH Research Resource for Biomedical AMS was originally funded at Lawrence Livermore National Laboratory in 1999 to develop and apply the technology of accelerator mass spectrometry (AMS) in broad- based biomedical research. The Resource’s niche is to fill needs for ultra high sensitivity quantitation when isotope-labeled agents are used. The Research Resource’s Technology Research and Development (TR&D) efforts will focus on the needs of the biomedical research community in the context of seven Driving Biomedical Projects (DBPs) that will drive the Center’s technical capabilities through three core TR&Ds. We will expand our present capabilities by developing a fully integrated HPLC AMS to increase our capabilities for metabolic measurements, we will develop methods to understand cellular processes and we will develop and validate methods for the application of AMS in human studies, which is a growing area of demand by collaborators and service users. In addition, we will continue to support new and ongoing collaborative and service projects that require the capabilities of the Resource. The Center will continue to train researchers in the use of the AMS capabilities being developed, and the results of all efforts will be widely disseminated to advance progress in biomedical research. Towards these goals, our specific aims are to:1.) Increase the value and information content of AMS measurements by combining molecular speciation with quantitation of defined macromolecular isolates. Specifically, develop and validate methods for macromolecule labeling, characterization and quantitation.2.) Develop and validate methods and strategies to enable AMS to become more broadly used in human studies. Specifically, demonstrate robust methods for conducting pharmacokinetic/pharmacodynamics studies in humans and model systems.3.) Increase the accessibility of AMS to the Biomedical research community and the throughput of AMS through direct coupling to separatory

Synergies and Distinctions between Computational Disciplines in Biomedical Research: Perspective from the Clinical and Translational Science Award Programs

Science.gov (United States)

Bernstam, Elmer V.; Hersh, William R.; Johnson, Stephen B.; Chute, Christopher G.; Nguyen, Hien; Sim, Ida; Nahm, Meredith; Weiner, Mark; Miller, Perry; DiLaura, Robert P.; Overcash, Marc; Lehmann, Harold P.; Eichmann, David; Athey, Brian D.; Scheuermann, Richard H.; Anderson, Nick; Starren, Justin B.; Harris, Paul A.; Smith, Jack W.; Barbour, Ed; Silverstein, Jonathan C.; Krusch, David A.; Nagarajan, Rakesh; Becich, Michael J.

2010-01-01

Clinical and translational research increasingly requires computation. Projects may involve multiple computationally-oriented groups including information technology (IT) professionals, computer scientists and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays and sub-optimal results. Although written from the perspective of clinical and translational science award (CTSA) programs within academic medical centers, the paper addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers. PMID:19550198

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.

Science.gov (United States)

Phan, John H; Quo, Chang F; Cheng, Chihwen; Wang, May Dongmei

2012-01-01

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

Extracting information from multiplex networks

Science.gov (United States)

Iacovacci, Jacopo; Bianconi, Ginestra

2016-06-01

Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ ˜ S for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

OpenCV-Based Nanomanipulation Information Extraction and the Probe Operation in SEM

Directory of Open Access Journals (Sweden)

Dongjie Li

2015-02-01

Full Text Available Aimed at the established telenanomanipulation system, the method of extracting location information and the strategies of probe operation were studied in this paper. First, the machine learning algorithm of OpenCV was used to extract location information from SEM images. Thus nanowires and probe in SEM images can be automatically tracked and the region of interest (ROI can be marked quickly. Then the location of nanowire and probe can be extracted from the ROI. To study the probe operation strategy, the Van der Waals force between probe and a nanowire was computed; thus relevant operating parameters can be obtained. With these operating parameters, the nanowire in 3D virtual environment can be preoperated and an optimal path of the probe can be obtained. The actual probe runs automatically under the telenanomanipulation system's control. Finally, experiments were carried out to verify the above methods, and results show the designed methods have achieved the expected effect.

A method for automating the extraction of specialized information from the web

NARCIS (Netherlands)

Lin, L.; Liotta, A.; Hippisley, A.; Hao, Y.; Liu, J.; Wang, Y.; Cheung, Y-M.; Yin, H.; Jiao, L.; Ma, j.; Jiao, Y-C.

2005-01-01

The World Wide Web can be viewed as a gigantic distributed database including millions of interconnected hosts some of which publish information via web servers or peer-to-peer systems. We present here a novel method for the extraction of semantically rich information from the web in a fully

Publishing priorities of biomedical research funders

Science.gov (United States)

Collins, Ellen

2013-01-01

Objectives To understand the publishing priorities, especially in relation to open access, of 10 UK biomedical research funders. Design Semistructured interviews. Setting 10 UK biomedical research funders. Participants 12 employees with responsibility for research management at 10 UK biomedical research funders; a purposive sample to represent a range of backgrounds and organisation types. Conclusions Publicly funded and large biomedical research funders are committed to open access publishing and are pleased with recent developments which have stimulated growth in this area. Smaller charitable funders are supportive of the aims of open access, but are concerned about the practical implications for their budgets and their funded researchers. Across the board, biomedical research funders are turning their attention to other priorities for sharing research outputs, including data, protocols and negative results. Further work is required to understand how smaller funders, including charitable funders, can support open access. PMID:24154520

Basics of biomedical ultrasound for engineers

CERN Document Server

Azhari, Haim

2010-01-01

"Basics of Biomedical Ultrasound for Engineers is a structured textbook for university engineering courses in biomedical ultrasound and for researchers in the field. This book offers a tool for building a solid understanding of biomedical ultrasound, and leads the novice through the field in a step-by-step manner. The book begins with the most basic definitions of waves, proceeds to ultrasounds in fluids, and then delves into solid ultrasounds, the most complicated kind of ultrasound. It encompasses a wide range of topics within biomedical ultrasound, from conceptual definitions of waves to the intricacies of focusing devices, transducers, and acoustic fields"--Provided by publisher.

Lignocellulosic Biomass Derived Functional Materials: Synthesis and Applications in Biomedical Engineering.

Science.gov (United States)

Zhang, Lei; Peng, Xinwen; Zhong, Linxin; Chua, Weitian; Xiang, Zhihua; Sun, Runcang

2017-09-18

The pertinent issue of resources shortage arising from global climate change in the recent years has accentuated the importance of materials that are environmental friendly. Despite the merits of current material like cellulose as the most abundant natural polysaccharide on earth, the incorporation of lignocellulosic biomass has the potential to value-add the recent development of cellulose-derivatives in drug delivery systems. Lignocellulosic biomass, with a hierarchical structure, comprised of cellulose, hemicellulose and lignin. As an excellent substrate that is renewable, biodegradable, biocompatible and chemically accessible for modified materials, lignocellulosic biomass sets forth a myriad of applications. To date, materials derived from lignocellulosic biomass have been extensively explored for new technological development and applications, such as biomedical, green electronics and energy products. In this review, chemical constituents of lignocellulosic biomass are first discussed before we critically examine the potential alternatives in the field of biomedical application. In addition, the pretreatment methods for extracting cellulose, hemicellulose and lignin from lignocellulosic biomass as well as their biological applications including drug delivery, biosensor, tissue engineering etc will be reviewed. It is anticipated there will be an increasing interest and research findings in cellulose, hemicellulose and lignin from natural resources, which help provide important directions for the development in biomedical applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Clustering cliques for graph-based summarization of the biomedical research literature

DEFF Research Database (Denmark)

Zhang, Han; Fiszman, Marcelo; Shin, Dongwook

2013-01-01

Background: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts).Results: Sem......Rep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm...

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra.

Science.gov (United States)

Schulze, H Georg; Turner, Robin F B

2015-06-01

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Misconduct Policies in High-Impact Biomedical Journals

Science.gov (United States)

Bosch, Xavier; Hernández, Cristina; Pericas, Juan M.; Doti, Pamela; Marušić, Ana

2012-01-01

Background It is not clear which research misconduct policies are adopted by biomedical journals. This study assessed the prevalence and content policies of the most influential biomedical journals on misconduct and procedures for handling and responding to allegations of misconduct. Methods We conducted a cross-sectional study of misconduct policies of 399 high-impact biomedical journals in 27 biomedical categories of the Journal Citation Reports in December 2011. Journal websites were reviewed for information relevant to misconduct policies. Results Of 399 journals, 140 (35.1%) provided explicit definitions of misconduct. Falsification was explicitly mentioned by 113 (28.3%) journals, fabrication by 104 (26.1%), plagiarism by 224 (56.1%), duplication by 242 (60.7%) and image manipulation by 154 (38.6%). Procedures for responding to misconduct were described in 179 (44.9%) websites, including retraction, (30.8%) and expression of concern (16.3%). Plagiarism-checking services were used by 112 (28.1%) journals. The prevalences of all types of misconduct policies were higher in journals that endorsed any policy from editors’ associations, Office of Research Integrity or professional societies compared to those that did not state adherence to these policy-producing bodies. Elsevier and Wiley-Blackwell had the most journals included (22.6% and 14.8%, respectively), with Wiley journals having greater a prevalence of misconduct definition and policies on falsification, fabrication and expression of concern and Elsevier of plagiarism-checking services. Conclusions Only a third of top-ranking peer-reviewed journals had publicly-available definitions of misconduct and less than a half described procedures for handling allegations of misconduct. As endorsement of international policies from policy-producing bodies was positively associated with implementation of policies and procedures, journals and their publishers should standardize their policies globally in order to

Misconduct policies in high-impact biomedical journals.

Directory of Open Access Journals (Sweden)

Xavier Bosch

Full Text Available It is not clear which research misconduct policies are adopted by biomedical journals. This study assessed the prevalence and content policies of the most influential biomedical journals on misconduct and procedures for handling and responding to allegations of misconduct.We conducted a cross-sectional study of misconduct policies of 399 high-impact biomedical journals in 27 biomedical categories of the Journal Citation Reports in December 2011. Journal websites were reviewed for information relevant to misconduct policies.Of 399 journals, 140 (35.1% provided explicit definitions of misconduct. Falsification was explicitly mentioned by 113 (28.3% journals, fabrication by 104 (26.1%, plagiarism by 224 (56.1%, duplication by 242 (60.7% and image manipulation by 154 (38.6%. Procedures for responding to misconduct were described in 179 (44.9% websites, including retraction, (30.8% and expression of concern (16.3%. Plagiarism-checking services were used by 112 (28.1% journals. The prevalences of all types of misconduct policies were higher in journals that endorsed any policy from editors' associations, Office of Research Integrity or professional societies compared to those that did not state adherence to these policy-producing bodies. Elsevier and Wiley-Blackwell had the most journals included (22.6% and 14.8%, respectively, with Wiley journals having greater a prevalence of misconduct definition and policies on falsification, fabrication and expression of concern and Elsevier of plagiarism-checking services.Only a third of top-ranking peer-reviewed journals had publicly-available definitions of misconduct and less than a half described procedures for handling allegations of misconduct. As endorsement of international policies from policy-producing bodies was positively associated with implementation of policies and procedures, journals and their publishers should standardize their policies globally in order to increase public trust in the

Green Synthesis of Robust, Biocompatible Silver Nanoparticles Using Garlic Extract

International Nuclear Information System (INIS)

White, G.V.; Kerscher, P.; Brown, R.M.; Morella, J.D.; Kitchens, C.L.; McAllister, W.; Dean, D.

2012-01-01

This paper details a facile approach for the synthesis of stable and monodisperse silver nanoparticles performed at ambient/low temperature, where Allium sativum (garlic) extract functions as the silver salt reducing agent during nanoparticle synthesis as well as the post synthesis stabilizing ligands. Varying the synthesis conditions provides control of particle size, size-distribution, and kinetics of particle formation. Infrared spectroscopy, energy dispersive X-ray chemical analysis, and high-performance liquid chromatography indicated that allicin and other carbohydrates in the garlic extract are the primary nanoparticle stabilizing moieties. The synthesized silver nanoparticles also demonstrate potential for biomedical applications, owing to (1) enhanced stability in biological media, (2) resistance to oxidation by the addition of H 2 O 2 , (3) ease and scalability of synthesis, and (4) lack of harsh chemicals required for synthesis. Cytotoxicity assays indicated no decrease in cellular proliferation for vascular smooth muscle cells and 3T3 fibroblasts at a concentration of 25 μg/mL, confirming that silver nanoparticles synthesized with garlic extract are potential candidates for future experimentation and implementation in the biomedical field.

Functionalized carbon nanotubes: biomedical applications

Science.gov (United States)

Vardharajula, Sandhya; Ali, Sk Z; Tiwari, Pooja M; Eroğlu, Erdal; Vig, Komal; Dennis, Vida A; Singh, Shree R

2012-01-01

Carbon nanotubes (CNTs) are emerging as novel nanomaterials for various biomedical applications. CNTs can be used to deliver a variety of therapeutic agents, including biomolecules, to the target disease sites. In addition, their unparalleled optical and electrical properties make them excellent candidates for bioimaging and other biomedical applications. However, the high cytotoxicity of CNTs limits their use in humans and many biological systems. The biocompatibility and low cytotoxicity of CNTs are attributed to size, dose, duration, testing systems, and surface functionalization. The functionalization of CNTs improves their solubility and biocompatibility and alters their cellular interaction pathways, resulting in much-reduced cytotoxic effects. Functionalized CNTs are promising novel materials for a variety of biomedical applications. These potential applications are particularly enhanced by their ability to penetrate biological membranes with relatively low cytotoxicity. This review is directed towards the overview of CNTs and their functionalization for biomedical applications with minimal cytotoxicity. PMID:23091380

Extraction of land cover change information from ENVISAT-ASAR data in Chengdu Plain

Science.gov (United States)

Xu, Wenbo; Fan, Jinlong; Huang, Jianxi; Tian, Yichen; Zhang, Yong

2006-10-01

Land cover data are essential to most global change research objectives, including the assessment of current environmental conditions and the simulation of future environmental scenarios that ultimately lead to public policy development. Chinese Academy of Sciences generated a nationwide land cover database in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in 1990s. In order to improve the reliability of the database, we will update the database anytime. But it is difficult to obtain remote sensing data to extract land cover change information in large-scale. It is hard to acquire optical remote sensing data in Chengdu plain, so the objective of this research was to evaluate multitemporal ENVISAT advanced synthetic aperture radar (ASAR) data for extracting land cover change information. Based on the fieldwork and the nationwide 1:100000 land cover database, the paper assesses several land cover changes in Chengdu plain, for example: crop to buildings, forest to buildings, and forest to bare land. The results show that ENVISAT ASAR data have great potential for the applications of extracting land cover change information.

Feasibility study for a biomedical experimental facility based on LEIR at CERN

International Nuclear Information System (INIS)

Abler, Daniel; Garonna, Adriano; Carli, Christian; Dosanjh, Manjit; Peach, Ken

2013-01-01

In light of the recent European developments in ion beam therapy, there is a strong interest from the biomedical research community to have more access to clinically relevant beams. Beamtime for pre-clinical studies is currently very limited and a new dedicated facility would allow extensive research into the radiobiological mechanisms of ion beam radiation and the development of more refined techniques of dosimetry and imaging. This basic research would support the current clinical efforts of the new treatment centres in Europe (for example HIT, CNAO and MedAustron). This paper presents first investigations on the feasibility of an experimental biomedical facility based on the CERN Low Energy Ion Ring LEIR accelerator. Such a new facility could provide beams of light ions (from protons to neon ions) in a collaborative and cost-effective way, since it would rely partly on CERN's competences and infrastructure. The main technical challenges linked to the implementation of a slow extraction scheme for LEIR and to the design of the experimental beamlines are described and first solutions presented. These include introducing new extraction septa into one of the straight sections of the synchrotron, changing the power supply configuration of the magnets, and designing a new horizontal beamline suitable for clinical beam energies, and a low-energy vertical beamline for particular radiobiological experiments. (author)

Feasibility study for a biomedical experimental facility based on LEIR at CERN.

Science.gov (United States)

Abler, Daniel; Garonna, Adriano; Carli, Christian; Dosanjh, Manjit; Peach, Ken

2013-07-01

In light of the recent European developments in ion beam therapy, there is a strong interest from the biomedical research community to have more access to clinically relevant beams. Beamtime for pre-clinical studies is currently very limited and a new dedicated facility would allow extensive research into the radiobiological mechanisms of ion beam radiation and the development of more refined techniques of dosimetry and imaging. This basic research would support the current clinical efforts of the new treatment centres in Europe (for example HIT, CNAO and MedAustron). This paper presents first investigations on the feasibility of an experimental biomedical facility based on the CERN Low Energy Ion Ring LEIR accelerator. Such a new facility could provide beams of light ions (from protons to neon ions) in a collaborative and cost-effective way, since it would rely partly on CERN's competences and infrastructure. The main technical challenges linked to the implementation of a slow extraction scheme for LEIR and to the design of the experimental beamlines are described and first solutions presented. These include introducing new extraction septa into one of the straight sections of the synchrotron, changing the power supply configuration of the magnets, and designing a new horizontal beamline suitable for clinical beam energies, and a low-energy vertical beamline for particular radiobiological experiments.

Professional Identification for Biomedical Engineers

Science.gov (United States)

Long, Francis M.

1973-01-01

Discusses four methods of professional identification in biomedical engineering including registration, certification, accreditation, and possible membership qualification of the societies. Indicates that the destiny of the biomedical engineer may be under the control of a new profession, neither the medical nor the engineering. (CC)

Egyptian Journal of Biomedical Sciences

African Journals Online (AJOL)

The Egyptian Journal of Biomedical Sciences publishes in all aspects of biomedical research sciences. Both basic and clinical research papers are welcomed. Vol 23 (2007). DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Table of Contents. Articles. Phytochemical And ...

An exploration of the biomedical optics course construction of undergraduate biomedical engineering program in medical colleges

Science.gov (United States)

Guo, Shijun; Lyu, Jie; Zhang, Peiming

2017-08-01

In this paper, the teaching goals, teaching contents and teaching methods in biomedical optics course construction are discussed. From the dimension of teaching goals, students should master the principle of optical inspection on the human body, diagnosis and treatment of methodology and instruments, through the study of the theory and practice of this course, and can utilize biomedical optics methods to solve practical problems in the clinical medical engineering practice. From the dimension of teaching contents, based on the characteristics of biomedical engineering in medical colleges, the organic integration of engineering aspects, medical optical instruments, and biomedical aspects dispersed in human anatomy, human physiology, clinical medicine fundamental related to the biomedical optics is build. Noninvasive measurement of the human body composition and noninvasive optical imaging of the human body were taken as actual problems in biomedical optics fields. Typical medical applications such as eye optics and laser medicine were also integrated into the theory and practice teaching. From the dimension of teaching methods, referencing to organ-system based medical teaching mode, optical principle and instrument principle were taught by teachers from school of medical instruments, and the histological characteristics and clinical actual need in areas such as digestive diseases and urinary surgery were taught by teachers from school of basic medicine or clinical medicine of medical colleges. Furthermore, clinical application guidance would be provided by physician and surgeons in hospitals.

Entity recognition in the biomedical domain using a hybrid approach.

Science.gov (United States)

Basaldella, Marco; Furrer, Lenz; Tasso, Carlo; Rinaldi, Fabio

2017-11-09

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

FigSum: automatically generating structured text summaries for figures in biomedical literature.

Science.gov (United States)

Agarwal, Shashank; Yu, Hong

2009-11-14

Figures are frequently used in biomedical articles to support research findings; however, they are often difficult to comprehend based on their legends alone and information from the full-text articles is required to fully understand them. Previously, we found that the information associated with a single figure is distributed throughout the full-text article the figure appears in. Here, we develop and evaluate a figure summarization system - FigSum, which aggregates this scattered information to improve figure comprehension. For each figure in an article, FigSum generates a structured text summary comprising one sentence from each of the four rhetorical categories - Introduction, Methods, Results and Discussion (IMRaD). The IMRaD category of sentences is predicted by an automated machine learning classifier. Our evaluation shows that FigSum captures 53% of the sentences in the gold standard summaries annotated by biomedical scientists and achieves an average ROUGE-1 score of 0.70, which is higher than a baseline system.

Evolving spectral transformations for multitemporal information extraction using evolutionary computation

Science.gov (United States)

Momm, Henrique; Easson, Greg

2011-01-01

Remote sensing plays an important role in assessing temporal changes in land features. The challenge often resides in the conversion of large quantities of raw data into actionable information in a timely and cost-effective fashion. To address this issue, research was undertaken to develop an innovative methodology integrating biologically-inspired algorithms with standard image classification algorithms to improve information extraction from multitemporal imagery. Genetic programming was used as the optimization engine to evolve feature-specific candidate solutions in the form of nonlinear mathematical expressions of the image spectral channels (spectral indices). The temporal generalization capability of the proposed system was evaluated by addressing the task of building rooftop identification from a set of images acquired at different dates in a cross-validation approach. The proposed system generates robust solutions (kappa values > 0.75 for stage 1 and > 0.4 for stage 2) despite the statistical differences between the scenes caused by land use and land cover changes coupled with variable environmental conditions, and the lack of radiometric calibration between images. Based on our results, the use of nonlinear spectral indices enhanced the spectral differences between features improving the clustering capability of standard classifiers and providing an alternative solution for multitemporal information extraction.

Extraction of Graph Information Based on Image Contents and the Use of Ontology

Science.gov (United States)

Kanjanawattana, Sarunya; Kimura, Masaomi

2016-01-01

A graph is an effective form of data representation used to summarize complex information. Explicit information such as the relationship between the X- and Y-axes can be easily extracted from a graph by applying human intelligence. However, implicit knowledge such as information obtained from other related concepts in an ontology also resides in…

Feasibility study for a biomedical experimental facility based on LEIR at CERN

CERN Document Server

Abler, Daniel; Carli, Christian; Dosanjh, Manjit; Peach, Ken; Orecchia, Roberto

2013-01-01

In light of the recent European developments in ion beam therapy, there is a strong interest from the biomedical research community to have more access to clinically relevant beams. Beamtime for pre-clinical studies is currently very limited and a new dedicated facility would allow extensive research into the radiobiological mechanisms of ion beam radiation and the development of more refined techniques of dosimetry and imaging. This basic research would support the current clinical efforts of the new treatment centres in Europe (for example HIT, CNAO and MedAustron). This paper presents first investigations on the feasibility of an experimental biomedical facility based on the CERN Low Energy Ion Ring LEIR accelerator. Such a new facility could provide beams of light ions (from protons to neon ions) in a collaborative and cost-effective way, since it would rely partly on CERN’s competences and infrastructure. The main technical challenges linked to the implementation of a slow extraction scheme for LEIR an...

Blended learning as an effective pedagogical paradigm for biomedical science

Directory of Open Access Journals (Sweden)

Perry Hartfield

2013-11-01

Full Text Available Blended learning combines face-to-face class based and online teaching and learning delivery in order to increase flexibility in how, when, and where students study and learn. The development, integration, and promotion of blended learning in frameworks of curriculum design can optimize the opportunities afforded by information and communication technologies and, concomitantly, accommodate a broad range of student learning styles. This study critically reviews the potential benefits of blended learning as a progressive educative paradigm for the teaching of biomedical science and evaluates the opportunities that blended learning offers for the delivery of accessible, flexible and sustainable teaching and learning experiences. A central tenet of biomedical science education at the tertiary level is the development of comprehensive hands-on practical competencies and technical skills (many of which require laboratory-based learning environments, and it is advanced that a blended learning model, which combines face-to-face synchronous teaching and learning activities with asynchronous online teaching and learning activities, effectively creates an authentic, enriching, and student-centred learning environment for biomedical science. Lastly, a blending learning design for introductory biochemistry will be described as an effective example of integrating face-to-face and online teaching, learning and assessment activities within the teaching domain of biomedical science.   DOI: 10.18870/hlrc.v3i4.169

A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

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Domonkos Tikk

Full Text Available The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study

African Journal of Biomedical Research

African Journals Online (AJOL)

The African Journal of biomedical Research was founded in 1998 as a joint project ... of the journal led to the formation of a group (Biomedical Communications Group, ... analysis of multidrug resistant aerobic gram-negative clinical isolates from a ... Dental formula and dental abnormalities observed in the Eidolon helvum ...

Special Issue: 3D Printing for Biomedical Engineering.

Science.gov (United States)

Chua, Chee Kai; Yeong, Wai Yee; An, Jia

2017-02-28

Three-dimensional (3D) printing has a long history of applications in biomedical engineering. The development and expansion of traditional biomedical applications are being advanced and enriched by new printing technologies. New biomedical applications such as bioprinting are highly attractive and trendy. This Special Issue aims to provide readers with a glimpse of the recent profile of 3D printing in biomedical research.

Decision-making and motivation to participate in biomedical research in southwest Nigeria.

Science.gov (United States)

Osamor, Pauline E; Kass, Nancy

2012-08-01

Motivations and decision-making styles that influence participation in biomedical research vary across study types, cultures, and countries. While there is a small amount of literature on informed consent in non-western cultures, few studies have examined how participants make the decision to join research. This study was designed to identify the factors motivating people to participate in biomedical research in a traditional Nigerian community, assess the degree to which participants involve others in the decision-making process, and examine issues of autonomy in decision-making for research. A descriptive cross-sectional study was conducted with 100 adults (50 men, 50 women) in an urban Nigerian community who had participated in a biomedical research study. Subjects were interviewed using a survey instrument. Two-thirds of the respondents reported participating in the biomedical study to learn more about their illness, while 30% hoped to get some medical care. Over three-quarters (78%) of participants discussed the enrollment decision with someone else and 39% reported obtaining permission from a spouse or family member to participate in the study. Women were more than twice as likely as men to report obtaining permission from someone else before participating. More specifically, half of the female participants reported seeking permission from a spouse before enrolling. The findings suggest that informed consent in this community is understood and practised as a relational activity that involves others in the decision making process. Further studies are needed in non-Western countries concerning autonomy, decision-making, and motivation to participate in research studies. © 2012 Blackwell Publishing Ltd.

Finding and Accessing Diagrams in Biomedical Publications

OpenAIRE

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar...

Biomedical Informatics Research and Education at the EuroMISE Center

Czech Academy of Sciences Publication Activity Database

Zvárová, Jana

2006-01-01

Roč. 45, Suppl. (2006), s. 166-173 ISSN 0026-1270 Grant - others:Evropské sociální fondy CZ04307/42011/0013 Institutional research plan: CEZ:AV0Z10300504 Keywords : biomedical informatics * research * education * healthcare * information society Subject RIV: BJ - Thermodynamics Impact factor: 1.684, year: 2006

An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction

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Yong Zhu

2015-01-01

Full Text Available After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority.

Publications in biomedical and environmental sciences programs, 1980

Energy Technology Data Exchange (ETDEWEB)

Pfuderer, H.A.; Moody, J.B.

1981-07-01

This bibliography contains 690 references to articles in journals, books, and reports published in the subject area of biomedical and environmental sciences during 1980. There are 529 references to articles published in journals and books and 161 references to reports. Staff members in the Biomedical and Environmental Sciences divisions have other publications not included in this bibliography; for example, theses, book reviews, abstracts published in journals or symposia proceedings, pending journal publications and reports such as monthly and bimonthly progress reports, contractor reports, and reports for internal distribution. This document is sorted by the division, and then alphabetically by author. The sorting by divisions separates the references by subject area in a simple way. The divisions represented in the order that they appear in the bibliography are Analytical Chemistry, Biology, Chemical Technology, Information R and D, Health and Safety Research, Energy, Environmental Sciences, and Computer Sciences.

Publications in biomedical and environmental sciences programs, 1980

International Nuclear Information System (INIS)

Pfuderer, H.A.; Moody, J.B.

1981-07-01

This bibliography contains 690 references to articles in journals, books, and reports published in the subject area of biomedical and environmental sciences during 1980. There are 529 references to articles published in journals and books and 161 references to reports. Staff members in the Biomedical and Environmental Sciences divisions have other publications not included in this bibliography; for example, theses, book reviews, abstracts published in journals or symposia proceedings, pending journal publications and reports such as monthly and bimonthly progress reports, contractor reports, and reports for internal distribution. This document is sorted by the division, and then alphabetically by author. The sorting by divisions separates the references by subject area in a simple way. The divisions represented in the order that they appear in the bibliography are Analytical Chemistry, Biology, Chemical Technology, Information R and D, Health and Safety Research, Energy, Environmental Sciences, and Computer Sciences

Sieve-based relation extraction of gene regulatory networks from biological literature.

Science.gov (United States)

Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

2015-01-01

Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles

Science.gov (United States)

Liu, Rey-Long

2015-01-01

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations. PMID:26440794

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles.

Directory of Open Access Journals (Sweden)

Rey-Long Liu

Full Text Available Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.

Special Issue: 3D Printing for Biomedical Engineering

Directory of Open Access Journals (Sweden)

Chee Kai Chua

2017-02-01

Full Text Available Three-dimensional (3D printing has a long history of applications in biomedical engineering. The development and expansion of traditional biomedical applications are being advanced and enriched by new printing technologies. New biomedical applications such as bioprinting are highly attractive and trendy. This Special Issue aims to provide readers with a glimpse of the recent profile of 3D printing in biomedical research.

Knowledge discovery: Extracting usable information from large amounts of data

International Nuclear Information System (INIS)

Whiteson, R.

1998-01-01

The threat of nuclear weapons proliferation is a problem of world wide concern. Safeguards are the key to nuclear nonproliferation and data is the key to safeguards. The safeguards community has access to a huge and steadily growing volume of data. The advantages of this data rich environment are obvious, there is a great deal of information which can be utilized. The challenge is to effectively apply proven and developing technologies to find and extract usable information from that data. That information must then be assessed and evaluated to produce the knowledge needed for crucial decision making. Efficient and effective analysis of safeguards data will depend on utilizing technologies to interpret the large, heterogeneous data sets that are available from diverse sources. With an order-of-magnitude increase in the amount of data from a wide variety of technical, textual, and historical sources there is a vital need to apply advanced computer technologies to support all-source analysis. There are techniques of data warehousing, data mining, and data analysis that can provide analysts with tools that will expedite their extracting useable information from the huge amounts of data to which they have access. Computerized tools can aid analysts by integrating heterogeneous data, evaluating diverse data streams, automating retrieval of database information, prioritizing inputs, reconciling conflicting data, doing preliminary interpretations, discovering patterns or trends in data, and automating some of the simpler prescreening tasks that are time consuming and tedious. Thus knowledge discovery technologies can provide a foundation of support for the analyst. Rather than spending time sifting through often irrelevant information, analysts could use their specialized skills in a focused, productive fashion. This would allow them to make their analytical judgments with more confidence and spend more of their time doing what they do best

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.

Research evaluation support services in biomedical libraries

Directory of Open Access Journals (Sweden)

Karen Elizabeth Gutzman

2018-01-01

Conclusions: Libraries can leverage a variety of evaluation support services as an opportunity to successfully meet an array of challenges confronting the biomedical research community, including robust efforts to report and demonstrate tangible and meaningful outcomes of biomedical research and clinical care. These services represent a transformative direction that can be emulated by other biomedical and research libraries.

Extraction of temporal information in functional MRI

Science.gov (United States)

Singh, M.; Sungkarat, W.; Jeong, Jeong-Won; Zhou, Yongxia

2002-10-01

The temporal resolution of functional MRI (fMRI) is limited by the shape of the haemodynamic response function (hrf) and the vascular architecture underlying the activated regions. Typically, the temporal resolution of fMRI is on the order of 1 s. We have developed a new data processing approach to extract temporal information on a pixel-by-pixel basis at the level of 100 ms from fMRI data. Instead of correlating or fitting the time-course of each pixel to a single reference function, which is the common practice in fMRI, we correlate each pixel's time-course to a series of reference functions that are shifted with respect to each other by 100 ms. The reference function yielding the highest correlation coefficient for a pixel is then used as a time marker for that pixel. A Monte Carlo simulation and experimental study of this approach were performed to estimate the temporal resolution as a function of signal-to-noise ratio (SNR) in the time-course of a pixel. Assuming a known and stationary hrf, the simulation and experimental studies suggest a lower limit in the temporal resolution of approximately 100 ms at an SNR of 3. The multireference function approach was also applied to extract timing information from an event-related motor movement study where the subjects flexed a finger on cue. The event was repeated 19 times with the event's presentation staggered to yield an approximately 100-ms temporal sampling of the haemodynamic response over the entire presentation cycle. The timing differences among different regions of the brain activated by the motor task were clearly visualized and quantified by this method. The results suggest that it is possible to achieve a temporal resolution of /spl sim/200 ms in practice with this approach.

Support patient search on pathology reports with interactive online learning based data extraction

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Shuai Zheng

2015-01-01

Full Text Available Background: Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user′s interaction with minimal human effort. Methods : We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system′s data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users′ corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. Results: We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of

Current practice of public involvement activities in biomedical research and innovation: a systematic qualitative review.

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Lander, Jonas; Hainz, Tobias; Hirschberg, Irene; Strech, Daniel

2014-01-01

A recent report from the British Nuffield Council on Bioethics associated 'emerging biotechnologies' with a threefold challenge: 1) uncertainty about outcomes, 2) diverse public views on the values and implications attached to biotechnologies and 3) the possibility of creating radical changes regarding societal relations and practices. To address these challenges, leading international institutions stress the need for public involvement activities (PIAs). The objective of this study was to assess the state of PIA reports in the field of biomedical research. PIA reports were identified via a systematic literature search. Thematic text analysis was employed for data extraction. After filtering, 35 public consultation and 11 public participation studies were included in this review. Analysis and synthesis of all 46 PIA studies resulted in 6 distinguishable PIA objectives and 37 corresponding PIA methods. Reports of outcome translation and PIA evaluation were found in 9 and 10 studies respectively (20% and 22%). The paper presents qualitative details. The state of PIAs on biomedical research and innovation is characterized by a broad range of methods and awkward variation in the wording of objectives. Better comparability of PIAs might improve the translation of PIA findings into further policy development. PIA-specific reporting guidelines would help in this regard. The modest level of translation efforts is another pointer to the "deliberation to policy gap". The results of this review could inform the design of new PIAs and future efforts to improve PIA comparability and outcome translation.

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

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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

INFORMATION EXTRACTION IN TOMB PIT USING HYPERSPECTRAL DATA

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X. Yang

2018-04-01

Full Text Available Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

Information Extraction in Tomb Pit Using Hyperspectral Data

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Yang, X.; Hou, M.; Lyu, S.; Ma, S.; Gao, Z.; Bai, S.; Gu, M.; Liu, Y.

2018-04-01

Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

A method for named entity normalization in biomedical articles: application to diseases and plants.

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Cho, Hyejin; Choi, Wonjun; Lee, Hyunju

2017-10-13

In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles. After NER is applied to articles, the next step is to normalize the identified names into standard concepts (i.e., disease names are mapped to the National Library of Medicine's Medical Subject Headings disease terms). In biomedical articles, many entity normalization methods rely on domain-specific dictionaries for resolving synonyms and abbreviations. However, the dictionaries are not comprehensive except for some entities such as genes. In recent years, biomedical articles have accumulated rapidly, and neural network-based algorithms that incorporate a large amount of unlabeled data have shown considerable success in several natural language processing problems. In this study, we propose an approach for normalizing biological entities, such as disease names and plant names, by using word embeddings to represent semantic spaces. For diseases, training data from the National Center for Biotechnology Information (NCBI) disease corpus and unlabeled data from PubMed abstracts were used to construct word representations. For plants, a training corpus that we manually constructed and unlabeled PubMed abstracts were used to represent word vectors. We showed that the proposed approach performed better than the use of only the training corpus or only the unlabeled data and showed that the normalization accuracy was improved by using our model even when the dictionaries were not comprehensive. We obtained F-scores of 0.808 and 0.690 for normalizing the NCBI disease corpus and manually constructed plant corpus, respectively. We further evaluated our approach using a data set in the disease normalization task of the BioCreative V challenge. When only the disease corpus was used as a dictionary, our approach significantly outperformed the best system of the task. The proposed approach shows robust

Grapefruit and its biomedical, antigenotoxic and chemopreventive properties.

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Cristóbal-Luna, José Melesio; Álvarez-González, Isela; Madrigal-Bujaidar, Eduardo; Chamorro-Cevallos, Germán

2018-02-01

Grapefruit (Citrus paradisi Mcfad) is a perenifolium tree 5-6 m high with a fruit of about 15 cm in diameter, protected by the peel we can find about 11-14 segments (carpels), each of which is surrounded by a membrane and each containing the juice sacs, as well as the seeds. The fruit is made up of numerous compounds, and is known to have nutritive value because of the presence of various vitamins and minerals, among other chemicals. The fruit is also used in the field of gastronomy. Information has been accumulated regarding the participation of the fruit structures in a variety of biomedical, antigenotoxic and chemopreventive effects, surely related with the presence of the numerous chemicals that have been determined to constitute the fruit. Such studies have been carried out in different in vitro and in vivo experimental models, and in a few human assays. The information published so far has shown interesting results, therefore, the aims of the present review are to initially examine the main characteristics of the fruit, followed by systematization of the acquired knowledge concerning the biomedical, antigenotoxic and chemopreventive effects produced by the three main structures of the fruit: peel, seed, and pulp. Copyright © 2018 Elsevier Ltd. All rights reserved.

BIOMEDICAL RESEARCH IN PSYCHIATRY AND RIGHT TO AUTONOMY OF PARTICIPANTS: COMPARATIVE REVIEW OF CROATIA AND EUROPE

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Kristijan Grđan

2017-01-01

Full Text Available Biomedical research is important for development of science, diagnostics and production of new, effective methods of treatment, with lowest possible risk. Biomedical research in human subjects opens number of legal and ethical issues, more often the issue of informed consent. Substitute provision of informed consent for unconscious persons or those who due to other reasons are not capable of giving consent is connected to number of controversies arising from abuse of rights. Recent international documents emphasize right to autonomy of persons with disabilities and require state parties to abolish guardianship regimes, which represents big challenges, especially in psychiatry. The purpose of this research was to determin how often psychiatric research in psychiatry is perform and to implement a comparative analysis of legislation in countries which conduct such research at most. The situation in Croatia has also been analyzed and review of effects of ban of szbstitute decision making given. The results showed that most of psychiatric biomedical research in Europe has been conducted for Altheimer’s disease and dementias, where informed consent is especially important. The comparative analysis showed that substitute informed consent is allowed in extraordinary situations, only for therapeutic purposes. Furthermore, the results direct the legislator in further possibilities of reforms in Croatian law.

Exploring subdomain variation in biomedical language

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Séaghdha Diarmuid Ó

2011-05-01

Full Text Available Abstract Background Applications of Natural Language Processing (NLP technology to biomedical texts have generated significant interest in recent years. In this paper we identify and investigate the phenomenon of linguistic subdomain variation within the biomedical domain, i.e., the extent to which different subject areas of biomedicine are characterised by different linguistic behaviour. While variation at a coarser domain level such as between newswire and biomedical text is well-studied and known to affect the portability of NLP systems, we are the first to conduct an extensive investigation into more fine-grained levels of variation. Results Using the large OpenPMC text corpus, which spans the many subdomains of biomedicine, we investigate variation across a number of lexical, syntactic, semantic and discourse-related dimensions. These dimensions are chosen for their relevance to the performance of NLP systems. We use clustering techniques to analyse commonalities and distinctions among the subdomains. Conclusions We find that while patterns of inter-subdomain variation differ somewhat from one feature set to another, robust clusters can be identified that correspond to intuitive distinctions such as that between clinical and laboratory subjects. In particular, subdomains relating to genetics and molecular biology, which are the most common sources of material for training and evaluating biomedical NLP tools, are not representative of all biomedical subdomains. We conclude that an awareness of subdomain variation is important when considering the practical use of language processing applications by biomedical researchers.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

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Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Semiconductor quantum dots: synthesis and water-solubilization for biomedical applications.

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Yu, William W

2008-10-01

Quantum dots (QDs) are generally nanosized inorganic particles. They have distinctive size-dependent optical properties due to their very small size (mostly semiconductor QDs (mainly metal-chalcogenide compounds) and forming biocompatible structures for biomedical applications are discussed in this paper. This information may facilitate the research to create new materials/technologies for future clinical applications.

Enhancing biomedical design with design thinking.

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Kemnitzer, Ronald; Dorsa, Ed

2009-01-01

The development of biomedical equipment is justifiably focused on making products that "work." However, this approach leaves many of the people affected by these designs (operators, patients, etc.) with little or no representation when it comes to the design of these products. Industrial design is a "user focused" profession which takes into account the needs of diverse groups when making design decisions. The authors propose that biomedical equipment design can be enhanced, made more user and patient "friendly" by adopting the industrial design approach to researching, analyzing, and ultimately designing biomedical products.

Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span

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Jordan MI

2006-05-01

Full Text Available Abstract Background The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made from analysis of molecular sequence and profiling data. Here, the potential of such modeling is demonstrated by examining the 5,225 free-text items in the Caenorhabditis Genetic Center (CGC Bibliography using techniques from statistical information retrieval. Items in the CGC biomedical text corpus were modeled using the Latent Dirichlet Allocation (LDA model. LDA is a hierarchical Bayesian model which represents a document as a random mixture over latent topics; each topic is characterized by a distribution over words. Results An LDA model estimated from CGC items had better predictive performance than two standard models (unigram and mixture of unigrams trained using the same data. To illustrate the practical utility of LDA models of biomedical corpora, a trained CGC LDA model was used for a retrospective study of nematode genes known to be associated with life span modification. Corpus-, document-, and word-level LDA parameters were combined with terms from the Gene Ontology to enhance the explanatory value of the CGC LDA model, and to suggest additional candidates for age-related genes. A novel, pairwise document similarity measure based on the posterior distribution on the topic simplex was formulated and used to search the CGC database for "homologs" of a "query" document discussing the life span-modifying clk-2 gene. Inspection of these document homologs enabled and facilitated the production of hypotheses about the function and role of clk-2. Conclusion Like other graphical models for genetic, genomic and other types of biological data, LDA provides a method for extracting unanticipated insights and generating predictions amenable to subsequent experimental validation.

Publications in biomedical and environmental sciences programs, 1981

Energy Technology Data Exchange (ETDEWEB)

Moody, J.B. (comp.)

1982-07-01

This bibliography contains 698 references to articles in journals, books, and reports published in the subject area of biomedical and environmental sciences during 1981. There are 520 references to articles published in journals and books and 178 references to reports. Staff members in the Biomedical and Environmental Sciences divisions have other publications not included in this bibliography; for example, theses, book reviews, abstracts published in journals or symposia proceedings, pending journal publications and reports such as monthly, bimonthly, and quarterly progress reports, contractor reports, and reports for internal distribution. This document is sorted by the division, and then alphabetically by author. The sorting by divisions separates the references by subject area in a simple way. The divisions represented in the order that they appear in the bibliography are Analytical Chemistry, Biology, Chemical Technology, Information R and D, Health and Safety Research, Instrumentation and Controls, Computer Sciences, Energy, Engineering Technology, Solid State, Central Management, Operations, and Environmental Sciences. Indexes are provided by author, title, and journal reference.

Publications in biomedical and environmental sciences programs, 1981

International Nuclear Information System (INIS)

Moody, J.B.

1982-07-01

This bibliography contains 698 references to articles in journals, books, and reports published in the subject area of biomedical and environmental sciences during 1981. There are 520 references to articles published in journals and books and 178 references to reports. Staff members in the Biomedical and Environmental Sciences divisions have other publications not included in this bibliography; for example, theses, book reviews, abstracts published in journals or symposia proceedings, pending journal publications and reports such as monthly, bimonthly, and quarterly progress reports, contractor reports, and reports for internal distribution. This document is sorted by the division, and then alphabetically by author. The sorting by divisions separates the references by subject area in a simple way. The divisions represented in the order that they appear in the bibliography are Analytical Chemistry, Biology, Chemical Technology, Information R and D, Health and Safety Research, Instrumentation and Controls, Computer Sciences, Energy, Engineering Technology, Solid State, Central Management, Operations, and Environmental Sciences. Indexes are provided by author, title, and journal reference

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

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