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Sample records for discovery approach based

  1. Fragment approaches in structure-based drug discovery

    International Nuclear Information System (INIS)

    Hubbard, Roderick E.

    2008-01-01

    Fragment-based methods are successfully generating novel and selective drug-like inhibitors of protein targets, with a number of groups reporting compounds entering clinical trials. This paper summarizes the key features of the approach as one of the tools in structure-guided drug discovery. There has been considerable interest recently in what is known as 'fragment-based lead discovery'. The novel feature of the approach is to begin with small low-affinity compounds. The main advantage is that a larger potential chemical diversity can be sampled with fewer compounds, which is particularly important for new target classes. The approach relies on careful design of the fragment library, a method that can detect binding of the fragment to the protein target, determination of the structure of the fragment bound to the target, and the conventional use of structural information to guide compound optimization. In this article the methods are reviewed, and experiences in fragment-based discovery of lead series of compounds against kinases such as PDK1 and ATPases such as Hsp90 are discussed. The examples illustrate some of the key benefits and issues of the approach and also provide anecdotal examples of the patterns seen in selectivity and the binding mode of fragments across different protein targets

  2. Development of Scientific Approach Based on Discovery Learning Module

    Science.gov (United States)

    Ellizar, E.; Hardeli, H.; Beltris, S.; Suharni, R.

    2018-04-01

    Scientific Approach is a learning process, designed to make the students actively construct their own knowledge through stages of scientific method. The scientific approach in learning process can be done by using learning modules. One of the learning model is discovery based learning. Discovery learning is a learning model for the valuable things in learning through various activities, such as observation, experience, and reasoning. In fact, the students’ activity to construct their own knowledge were not optimal. It’s because the available learning modules were not in line with the scientific approach. The purpose of this study was to develop a scientific approach discovery based learning module on Acid Based, also on electrolyte and non-electrolyte solution. The developing process of this chemistry modules use the Plomp Model with three main stages. The stages are preliminary research, prototyping stage, and the assessment stage. The subject of this research was the 10th and 11th Grade of Senior High School students (SMAN 2 Padang). Validation were tested by the experts of Chemistry lecturers and teachers. Practicality of these modules had been tested through questionnaire. The effectiveness had been tested through experimental procedure by comparing student achievement between experiment and control groups. Based on the findings, it can be concluded that the developed scientific approach discovery based learning module significantly improve the students’ learning in Acid-based and Electrolyte solution. The result of the data analysis indicated that the chemistry module was valid in content, construct, and presentation. Chemistry module also has a good practicality level and also accordance with the available time. This chemistry module was also effective, because it can help the students to understand the content of the learning material. That’s proved by the result of learning student. Based on the result can conclude that chemistry module based on

  3. A collaborative filtering-based approach to biomedical knowledge discovery.

    Science.gov (United States)

    Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

    2018-02-15

    The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  4. Fragment-based approaches to the discovery of kinase inhibitors.

    Science.gov (United States)

    Mortenson, Paul N; Berdini, Valerio; O'Reilly, Marc

    2014-01-01

    Protein kinases are one of the most important families of drug targets, and aberrant kinase activity has been linked to a large number of disease areas. Although eminently targetable using small molecules, kinases present a number of challenges as drug targets, not least obtaining selectivity across such a large and relatively closely related target family. Fragment-based drug discovery involves screening simple, low-molecular weight compounds to generate initial hits against a target. These hits are then optimized to more potent compounds via medicinal chemistry, usually facilitated by structural biology. Here, we will present a number of recent examples of fragment-based approaches to the discovery of kinase inhibitors, detailing the construction of fragment-screening libraries, the identification and validation of fragment hits, and their optimization into potent and selective lead compounds. The advantages of fragment-based methodologies will be discussed, along with some of the challenges associated with using this route. Finally, we will present a number of key lessons derived both from our own experience running fragment screens against kinases and from a large number of published studies.

  5. Hierarchical virtual screening approaches in small molecule drug discovery.

    Science.gov (United States)

    Kumar, Ashutosh; Zhang, Kam Y J

    2015-01-01

    Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Discovery of Boolean metabolic networks: integer linear programming based approach.

    Science.gov (United States)

    Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

    2018-04-11

    Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

  7. Introduction to fragment-based drug discovery.

    Science.gov (United States)

    Erlanson, Daniel A

    2012-01-01

    Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.

  8. Argo_CUDA: Exhaustive GPU based approach for motif discovery in large DNA datasets.

    Science.gov (United States)

    Vishnevsky, Oleg V; Bocharnikov, Andrey V; Kolchanov, Nikolay A

    2018-02-01

    The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top "peak" ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.

  9. A Ligand-observed Mass Spectrometry Approach Integrated into the Fragment Based Lead Discovery Pipeline

    Science.gov (United States)

    Chen, Xin; Qin, Shanshan; Chen, Shuai; Li, Jinlong; Li, Lixin; Wang, Zhongling; Wang, Quan; Lin, Jianping; Yang, Cheng; Shui, Wenqing

    2015-01-01

    In fragment-based lead discovery (FBLD), a cascade combining multiple orthogonal technologies is required for reliable detection and characterization of fragment binding to the target. Given the limitations of the mainstream screening techniques, we presented a ligand-observed mass spectrometry approach to expand the toolkits and increase the flexibility of building a FBLD pipeline especially for tough targets. In this study, this approach was integrated into a FBLD program targeting the HCV RNA polymerase NS5B. Our ligand-observed mass spectrometry analysis resulted in the discovery of 10 hits from a 384-member fragment library through two independent screens of complex cocktails and a follow-up validation assay. Moreover, this MS-based approach enabled quantitative measurement of weak binding affinities of fragments which was in general consistent with SPR analysis. Five out of the ten hits were then successfully translated to X-ray structures of fragment-bound complexes to lay a foundation for structure-based inhibitor design. With distinctive strengths in terms of high capacity and speed, minimal method development, easy sample preparation, low material consumption and quantitative capability, this MS-based assay is anticipated to be a valuable addition to the repertoire of current fragment screening techniques. PMID:25666181

  10. Fragment-based approaches to anti-HIV drug discovery: state of the art and future opportunities.

    Science.gov (United States)

    Huang, Boshi; Kang, Dongwei; Zhan, Peng; Liu, Xinyong

    2015-12-01

    The search for additional drugs to treat HIV infection is a continuing effort due to the emergence and spread of HIV strains resistant to nearly all current drugs. The recent literature reveals that fragment-based drug design/discovery (FBDD) has become an effective alternative to conventional high-throughput screening strategies for drug discovery. In this critical review, the authors describe the state of the art in FBDD strategies for the discovery of anti-HIV drug-like compounds. The article focuses on fragment screening techniques, direct fragment-based design and early hit-to-lead progress. Rapid progress in biophysical detection and in silico techniques has greatly aided the application of FBDD to discover candidate agents directed at a variety of anti-HIV targets. Growing evidence suggests that structural insights on key proteins in the HIV life cycle can be applied in the early phase of drug discovery campaigns, providing valuable information on the binding modes and efficiently prompting fragment hit-to-lead progression. The combination of structural insights with improved methodologies for FBDD, including the privileged fragment-based reconstruction approach, fragment hybridization based on crystallographic overlays, fragment growth exploiting dynamic combinatorial chemistry, and high-speed fragment assembly via diversity-oriented synthesis followed by in situ screening, offers the possibility of more efficient and rapid discovery of novel drugs for HIV-1 prevention or treatment. Though the use of FBDD in anti-HIV drug discovery is still in its infancy, it is anticipated that anti-HIV agents developed via fragment-based strategies will be introduced into the clinic in the future.

  11. Network-based approaches to climate knowledge discovery

    Science.gov (United States)

    Budich, Reinhard; Nyberg, Per; Weigel, Tobias

    2011-11-01

    Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

  12. Facilitating Students' Interaction with Real Gas Properties Using a Discovery-Based Approach and Molecular Dynamics Simulations

    Science.gov (United States)

    Sweet, Chelsea; Akinfenwa, Oyewumi; Foley, Jonathan J., IV

    2018-01-01

    We present an interactive discovery-based approach to studying the properties of real gases using simple, yet realistic, molecular dynamics software. Use of this approach opens up a variety of opportunities for students to interact with the behaviors and underlying theories of real gases. Students can visualize gas behavior under a variety of…

  13. Computational neuropharmacology: dynamical approaches in drug discovery.

    Science.gov (United States)

    Aradi, Ildiko; Erdi, Péter

    2006-05-01

    Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

  14. Discovery learning with SAVI approach in geometry learning

    Science.gov (United States)

    Sahara, R.; Mardiyana; Saputro, D. R. S.

    2018-05-01

    Geometry is one branch of mathematics that an important role in learning mathematics in the schools. This research aims to find out about Discovery Learning with SAVI approach to achievement of learning geometry. This research was conducted at Junior High School in Surakarta city. Research data were obtained through test and questionnaire. Furthermore, the data was analyzed by using two-way Anova. The results showed that Discovery Learning with SAVI approach gives a positive influence on mathematics learning achievement. Discovery Learning with SAVI approach provides better mathematics learning outcomes than direct learning. In addition, students with high self-efficacy categories have better mathematics learning achievement than those with moderate and low self-efficacy categories, while student with moderate self-efficacy categories are better mathematics learning achievers than students with low self-efficacy categories. There is an interaction between Discovery Learning with SAVI approach and self-efficacy toward student's mathematics learning achievement. Therefore, Discovery Learning with SAVI approach can improve mathematics learning achievement.

  15. Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach.

    Science.gov (United States)

    Own, Chung-Ming; Meng, Zhaopeng; Liu, Kehan

    2015-09-03

    Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs) and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS), which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.

  16. Handling Neighbor Discovery and Rendezvous Consistency with Weighted Quorum-Based Approach

    Directory of Open Access Journals (Sweden)

    Chung-Ming Own

    2015-09-01

    Full Text Available Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS, which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.

  17. A Wavelet-Based Approach to Pattern Discovery in Melodies

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David; Weyde, Tillman

    2016-01-01

    We present a computational method for pattern discovery based on the application of the wavelet transform to symbolic representations of melodies or monophonic voices. We model the importance of a discovered pattern in terms of the compression ratio that can be achieved by using it to describe...

  18. Proteomic and metabolomic approaches to biomarker discovery

    CERN Document Server

    Issaq, Haleem J

    2013-01-01

    Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution.  The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis...

  19. Exploring relation types for literature-based discovery.

    Science.gov (United States)

    Preiss, Judita; Stevenson, Mark; Gaizauskas, Robert

    2015-09-01

    Literature-based discovery (LBD) aims to identify "hidden knowledge" in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques rely on linguistic analysis, for example, shallow parsing, to identify semantically stronger relations. Such approaches generate fewer hypotheses, but may miss hidden knowledge. The authors investigate this trade-off in detail, comparing techniques for identifying related concepts to discover which are most suitable for LBD. A generic LBD system that can utilize a range of relation types was developed. Experiments were carried out comparing a number of techniques for identifying relations. Two approaches were used for evaluation: replication of existing discoveries and the "time slicing" approach.(1) RESULTS: Previous LBD discoveries could be replicated using relations based either on document co-occurrence or linguistic analysis. Using relations based on linguistic analysis generated many fewer hypotheses, but a significantly greater proportion of them were candidates for hidden knowledge. The use of linguistic analysis-based relations improves accuracy of LBD without overly damaging coverage. LBD systems often generate huge numbers of hypotheses, which are infeasible to manually review. Improving their accuracy has the potential to make these systems significantly more usable. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  20. Computational Design and Discovery of Ni-Based Alloys and Coatings: Thermodynamic Approaches Validated by Experiments

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Zi-Kui [Pennsylvania State University; Gleeson, Brian [University of Pittsburgh; Shang, Shunli [Pennsylvania State University; Gheno, Thomas [University of Pittsburgh; Lindwall, Greta [Pennsylvania State University; Zhou, Bi-Cheng [Pennsylvania State University; Liu, Xuan [Pennsylvania State University; Ross, Austin [Pennsylvania State University

    2018-04-23

    This project developed computational tools that can complement and support experimental efforts in order to enable discovery and more efficient development of Ni-base structural materials and coatings. The project goal was reached through an integrated computation-predictive and experimental-validation approach, including first-principles calculations, thermodynamic CALPHAD (CALculation of PHAse Diagram), and experimental investigations on compositions relevant to Ni-base superalloys and coatings in terms of oxide layer growth and microstructure stabilities. The developed description included composition ranges typical for coating alloys and, hence, allow for prediction of thermodynamic properties for these material systems. The calculation of phase compositions, phase fraction, and phase stabilities, which are directly related to properties such as ductility and strength, was a valuable contribution, along with the collection of computational tools that are required to meet the increasing demands for strong, ductile and environmentally-protective coatings. Specifically, a suitable thermodynamic description for the Ni-Al-Cr-Co-Si-Hf-Y system was developed for bulk alloy and coating compositions. Experiments were performed to validate and refine the thermodynamics from the CALPHAD modeling approach. Additionally, alloys produced using predictions from the current computational models were studied in terms of their oxidation performance. Finally, results obtained from experiments aided in the development of a thermodynamic modeling automation tool called ESPEI/pycalphad - for more rapid discovery and development of new materials.

  1. Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

    Science.gov (United States)

    Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi

    2017-09-01

    The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

  2. Keefektifan setting TPS dalam pendekatan discovery learning dan problem-based learning pada pembelajaran materi lingkaran SMP

    Directory of Open Access Journals (Sweden)

    Rahmi Hidayati

    2017-05-01

    The purpose of this study was to describe the effectiveness of setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning in terms of student achievement, mathematical communication skills, and interpersonal skills of the student.  This study was a quasi-experimental study using the pretest-posttest nonequivalent group design. The research population comprised all Year VIII students of SMP Negeri 1 Yogyakarta. The research sample was randomly selected from eight classes, two classes were elected. The instrument used in this study is the learning achievement test, a test of mathematical communication skills, and interpersonal skills student questionnaires. To test the effectiveness of setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning, the one sample t-test was carried out. Then, to investigate the difference in effectiveness between the setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning, the Multivariate Analysis of Variance (MANOVA was carried out. The research findings indicate that the setting TPS discovery approach to learning and problem-based approach to learning (PBL is effective in terms of learning achievement, mathematical communication skills, and interpersonal skills of the students. No difference in effectiveness between setting TPS discovery approach to learning and problem-based learning (PBL in terms of learning achievement, mathematical communication skills, and interpersonal skills of the students. Keywords: TPS setting in discovery learning approach, in problem-based learning, academic achievement, mathematical communication skills, and interpersonal skills of the student

  3. Biomarker discovery in mass spectrometry-based urinary proteomics.

    Science.gov (United States)

    Thomas, Samuel; Hao, Ling; Ricke, William A; Li, Lingjun

    2016-04-01

    Urinary proteomics has become one of the most attractive topics in disease biomarker discovery. MS-based proteomic analysis has advanced continuously and emerged as a prominent tool in the field of clinical bioanalysis. However, only few protein biomarkers have made their way to validation and clinical practice. Biomarker discovery is challenged by many clinical and analytical factors including, but not limited to, the complexity of urine and the wide dynamic range of endogenous proteins in the sample. This article highlights promising technologies and strategies in the MS-based biomarker discovery process, including study design, sample preparation, protein quantification, instrumental platforms, and bioinformatics. Different proteomics approaches are discussed, and progresses in maximizing urinary proteome coverage and standardization are emphasized in this review. MS-based urinary proteomics has great potential in the development of noninvasive diagnostic assays in the future, which will require collaborative efforts between analytical scientists, systems biologists, and clinicians. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Medicinal chemistry inspired fragment-based drug discovery.

    Science.gov (United States)

    Lanter, James; Zhang, Xuqing; Sui, Zhihua

    2011-01-01

    Lead generation can be a very challenging phase of the drug discovery process. The two principal methods for this stage of research are blind screening and rational design. Among the rational or semirational design approaches, fragment-based drug discovery (FBDD) has emerged as a useful tool for the generation of lead structures. It is particularly powerful as a complement to high-throughput screening approaches when the latter failed to yield viable hits for further development. Engagement of medicinal chemists early in the process can accelerate the progression of FBDD efforts by incorporating drug-friendly properties in the earliest stages of the design process. Medium-chain acyl-CoA synthetase 2b and ketohexokinase are chosen as examples to illustrate the importance of close collaboration of medicinal chemists, crystallography, and modeling. Copyright © 2011 Elsevier Inc. All rights reserved.

  5. From crystal to compound: structure-based antimalarial drug discovery.

    Science.gov (United States)

    Drinkwater, Nyssa; McGowan, Sheena

    2014-08-01

    Despite a century of control and eradication campaigns, malaria remains one of the world's most devastating diseases. Our once-powerful therapeutic weapons are losing the war against the Plasmodium parasite, whose ability to rapidly develop and spread drug resistance hamper past and present malaria-control efforts. Finding new and effective treatments for malaria is now a top global health priority, fuelling an increase in funding and promoting open-source collaborations between researchers and pharmaceutical consortia around the world. The result of this is rapid advances in drug discovery approaches and technologies, with three major methods for antimalarial drug development emerging: (i) chemistry-based, (ii) target-based, and (iii) cell-based. Common to all three of these approaches is the unique ability of structural biology to inform and accelerate drug development. Where possible, SBDD (structure-based drug discovery) is a foundation for antimalarial drug development programmes, and has been invaluable to the development of a number of current pre-clinical and clinical candidates. However, as we expand our understanding of the malarial life cycle and mechanisms of resistance development, SBDD as a field must continue to evolve in order to develop compounds that adhere to the ideal characteristics for novel antimalarial therapeutics and to avoid high attrition rates pre- and post-clinic. In the present review, we aim to examine the contribution that SBDD has made to current antimalarial drug development efforts, covering hit discovery to lead optimization and prevention of parasite resistance. Finally, the potential for structural biology, particularly high-throughput structural genomics programmes, to identify future targets for drug discovery are discussed.

  6. A Bioinorganic Approach to Fragment-Based Drug Discovery Targeting Metalloenzymes.

    Science.gov (United States)

    Cohen, Seth M

    2017-08-15

    Metal-dependent enzymes (i.e., metalloenzymes) make up a large fraction of all enzymes and are critically important in a wide range of biological processes, including DNA modification, protein homeostasis, antibiotic resistance, and many others. Consequently, metalloenzymes represent a vast and largely untapped space for drug development. The discovery of effective therapeutics that target metalloenzymes lies squarely at the interface of bioinorganic and medicinal chemistry and requires expertise, methods, and strategies from both fields to mount an effective campaign. In this Account, our research program that brings together the principles and methods of bioinorganic and medicinal chemistry are described, in an effort to bridge the gap between these fields and address an important class of medicinal targets. Fragment-based drug discovery (FBDD) is an important drug discovery approach that is particularly well suited for metalloenzyme inhibitor development. FBDD uses relatively small but diverse chemical structures that allow for the assembly of privileged molecular collections that focus on a specific feature of the target enzyme. For metalloenzyme inhibition, the specific feature is rather obvious, namely, a metal-dependent active site. Surprisingly, prior to our work, the exploration of diverse molecular fragments for binding the metal active sites of metalloenzymes was largely unexplored. By assembling a modest library of metal-binding pharmacophores (MBPs), we have been able to find lead hits for many metalloenzymes and, from these hits, develop inhibitors that act via novel mechanisms of action. A specific case study on the use of this strategy to identify a first-in-class inhibitor of zinc-dependent Rpn11 (a component of the proteasome) is highlighted. The application of FBDD for the development of metalloenzyme inhibitors has raised several other compelling questions, such as how the metalloenzyme active site influences the coordination chemistry of bound

  7. Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

    Science.gov (United States)

    Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

    Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object

  8. Use of machine learning approaches for novel drug discovery.

    Science.gov (United States)

    Lima, Angélica Nakagawa; Philot, Eric Allison; Trossini, Gustavo Henrique Goulart; Scott, Luis Paulo Barbour; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria

    2016-01-01

    The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

  9. Data Mining and Knowledge Discovery via Logic-Based Methods

    CERN Document Server

    Triantaphyllou, Evangelos

    2010-01-01

    There are many approaches to data mining and knowledge discovery (DM&KD), including neural networks, closest neighbor methods, and various statistical methods. This monograph, however, focuses on the development and use of a novel approach, based on mathematical logic, that the author and his research associates have worked on over the last 20 years. The methods presented in the book deal with key DM&KD issues in an intuitive manner and in a natural sequence. Compared to other DM&KD methods, those based on mathematical logic offer a direct and often intuitive approach for extracting easily int

  10. Novel approach of fragment-based lead discovery applied to renin inhibitors.

    Science.gov (United States)

    Tawada, Michiko; Suzuki, Shinkichi; Imaeda, Yasuhiro; Oki, Hideyuki; Snell, Gyorgy; Behnke, Craig A; Kondo, Mitsuyo; Tarui, Naoki; Tanaka, Toshimasa; Kuroita, Takanobu; Tomimoto, Masaki

    2016-11-15

    A novel approach was conducted for fragment-based lead discovery and applied to renin inhibitors. The biochemical screening of a fragment library against renin provided the hit fragment which showed a characteristic interaction pattern with the target protein. The hit fragment bound only to the S1, S3, and S3 SP (S3 subpocket) sites without any interactions with the catalytic aspartate residues (Asp32 and Asp215 (pepsin numbering)). Prior to making chemical modifications to the hit fragment, we first identified its essential binding sites by utilizing the hit fragment's substructures. Second, we created a new and smaller scaffold, which better occupied the identified essential S3 and S3 SP sites, by utilizing library synthesis with high-throughput chemistry. We then revisited the S1 site and efficiently explored a good building block attaching to the scaffold with library synthesis. In the library syntheses, the binding modes of each pivotal compound were determined and confirmed by X-ray crystallography and the library was strategically designed by structure-based computational approach not only to obtain a more active compound but also to obtain informative Structure Activity Relationship (SAR). As a result, we obtained a lead compound offering synthetic accessibility as well as the improved in vitro ADMET profiles. The fragments and compounds possessing a characteristic interaction pattern provided new structural insights into renin's active site and the potential to create a new generation of renin inhibitors. In addition, we demonstrated our FBDD strategy integrating highly sensitive biochemical assay, X-ray crystallography, and high-throughput synthesis and in silico library design aimed at fragment morphing at the initial stage was effective to elucidate a pocket profile and a promising lead compound. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

    Science.gov (United States)

    de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

    2017-12-01

    CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

  12. Advances in fragment-based drug discovery platforms.

    Science.gov (United States)

    Orita, Masaya; Warizaya, Masaichi; Amano, Yasushi; Ohno, Kazuki; Niimi, Tatsuya

    2009-11-01

    Fragment-based drug discovery (FBDD) has been established as a powerful alternative and complement to traditional high-throughput screening techniques for identifying drug leads. At present, this technique is widely used among academic groups as well as small biotech and large pharmaceutical companies. In recent years, > 10 new compounds developed with FBDD have entered clinical development, and more and more attention in the drug discovery field is being focused on this technique. Under the FBDD approach, a fragment library of relatively small compounds (molecular mass = 100 - 300 Da) is screened by various methods and the identified fragment hits which normally weakly bind to the target are used as starting points to generate more potent drug leads. Because FBDD is still a relatively new drug discovery technology, further developments and optimizations in screening platforms and fragment exploitation can be expected. This review summarizes recent advances in FBDD platforms and discusses the factors important for the successful application of this technique. Under the FBDD approach, both identifying the starting fragment hit to be developed and generating the drug lead from that starting fragment hit are important. Integration of various techniques, such as computational technology, X-ray crystallography, NMR, surface plasmon resonance, isothermal titration calorimetry, mass spectrometry and high-concentration screening, must be applied in a situation-appropriate manner.

  13. [Fragment-based drug discovery: concept and aim].

    Science.gov (United States)

    Tanaka, Daisuke

    2010-03-01

    Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.

  14. Bead-based screening in chemical biology and drug discovery

    DEFF Research Database (Denmark)

    Komnatnyy, Vitaly V.; Nielsen, Thomas Eiland; Qvortrup, Katrine

    2018-01-01

    libraries for early drug discovery. Among the various library forms, the one-bead-one-compound (OBOC) library, where each bead carries many copies of a single compound, holds the greatest potential for the rapid identification of novel hits against emerging drug targets. However, this potential has not yet...... been fully realized due to a number of technical obstacles. In this feature article, we review the progress that has been made towards bead-based library screening and applications to the discovery of bioactive compounds. We identify the key challenges of this approach and highlight key steps needed......High-throughput screening is an important component of the drug discovery process. The screening of libraries containing hundreds of thousands of compounds requires assays amanable to miniaturisation and automization. Combinatorial chemistry holds a unique promise to deliver structural diverse...

  15. Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

    Science.gov (United States)

    Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

    2010-01-01

    In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

  16. SECURE SERVICE DISCOVERY BASED ON PROBE PACKET MECHANISM FOR MANETS

    Directory of Open Access Journals (Sweden)

    S. Pariselvam

    2015-03-01

    Full Text Available In MANETs, Service discovery process is always considered to be crucial since they do not possess a centralized infrastructure for communication. Moreover, different services available through the network necessitate varying categories. Hence, a need arises for devising a secure probe based service discovery mechanism to reduce the complexity in providing the services to the network users. In this paper, we propose a Secure Service Discovery Based on Probe Packet Mechanism (SSDPPM for identifying the DoS attack in MANETs, which depicts a new approach for estimating the level of trust present in each and every routing path of a mobile ad hoc network by using probe packets. Probing based service discovery mechanisms mainly identifies a mobile node’s genuineness using a test packet called probe that travels the entire network for the sake of computing the degree of trust maintained between the mobile nodes and it’s attributed impact towards the network performance. The performance of SSDPPM is investigated through a wide range of network related parameters like packet delivery, throughput, Control overhead and total overhead using the version ns-2.26 network simulator. This mechanism SSDPPM, improves the performance of the network in an average by 23% and 19% in terms of packet delivery ratio and throughput than the existing service discovery mechanisms available in the literature.

  17. Experiences in fragment-based drug discovery.

    Science.gov (United States)

    Murray, Christopher W; Verdonk, Marcel L; Rees, David C

    2012-05-01

    Fragment-based drug discovery (FBDD) has become established in both industry and academia as an alternative approach to high-throughput screening for the generation of chemical leads for drug targets. In FBDD, specialised detection methods are used to identify small chemical compounds (fragments) that bind to the drug target, and structural biology is usually employed to establish their binding mode and to facilitate their optimisation. In this article, we present three recent and successful case histories in FBDD. We then re-examine the key concepts and challenges of FBDD with particular emphasis on recent literature and our own experience from a substantial number of FBDD applications. Our opinion is that careful application of FBDD is living up to its promise of delivering high quality leads with good physical properties and that in future many drug molecules will be derived from fragment-based approaches. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Net present value approaches for drug discovery.

    Science.gov (United States)

    Svennebring, Andreas M; Wikberg, Jarl Es

    2013-12-01

    Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.

  19. Resource Discovery in Activity-Based Sensor Networks

    DEFF Research Database (Denmark)

    Bucur, Doina; Bardram, Jakob

    This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networ....... ABSN enhances the generic Extended Zone Routing Protocol with logical sensor grouping and greatly lowers network overhead during the process of discovery, while keeping discovery latency close to optimal.......This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

  20. Solution NMR Spectroscopy in Target-Based Drug Discovery.

    Science.gov (United States)

    Li, Yan; Kang, Congbao

    2017-08-23

    Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.

  1. Fragment-based drug discovery and protein–protein interactions

    Directory of Open Access Journals (Sweden)

    Turnbull AP

    2014-09-01

    Full Text Available Andrew P Turnbull,1 Susan M Boyd,2 Björn Walse31CRT Discovery Laboratories, Department of Biological Sciences, Birkbeck, University of London, London, UK; 2IOTA Pharmaceuticals Ltd, Cambridge, UK; 3SARomics Biostructures AB, Lund, SwedenAbstract: Protein–protein interactions (PPIs are involved in many biological processes, with an estimated 400,000 PPIs within the human proteome. There is significant interest in exploiting the relatively unexplored potential of these interactions in drug discovery, driven by the need to find new therapeutic targets. Compared with classical drug discovery against targets with well-defined binding sites, developing small-molecule inhibitors against PPIs where the contact surfaces are frequently more extensive and comparatively flat, with most of the binding energy localized in “hot spots”, has proven far more challenging. However, despite the difficulties associated with targeting PPIs, important progress has been made in recent years with fragment-based drug discovery playing a pivotal role in improving their tractability. Computational and empirical approaches can be used to identify hot-spot regions and assess the druggability and ligandability of new targets, whilst fragment screening campaigns can detect low-affinity fragments that either directly or indirectly perturb the PPI. Once fragment hits have been identified and confirmed using biochemical and biophysical approaches, three-dimensional structural data derived from nuclear magnetic resonance or X-ray crystallography can be used to drive medicinal chemistry efforts towards the development of more potent inhibitors. A small-scale comparison presented in this review of “standard” fragments with those targeting PPIs has revealed that the latter tend to be larger, be more lipophilic, and contain more polar (acid/base functionality, whereas three-dimensional descriptor data indicate that there is little difference in their three

  2. Fragment-based drug discovery using rational design.

    Science.gov (United States)

    Jhoti, H

    2007-01-01

    Fragment-based drug discovery (FBDD) is established as an alternative approach to high-throughput screening for generating novel small molecule drug candidates. In FBDD, relatively small libraries of low molecular weight compounds (or fragments) are screened using sensitive biophysical techniques to detect their binding to the target protein. A lower absolute affinity of binding is expected from fragments, compared to much higher molecular weight hits detected by high-throughput screening, due to their reduced size and complexity. Through the use of iterative cycles of medicinal chemistry, ideally guided by three-dimensional structural data, it is often then relatively straightforward to optimize these weak binding fragment hits into potent and selective lead compounds. As with most other lead discovery methods there are two key components of FBDD; the detection technology and the compound library. In this review I outline the two main approaches used for detecting the binding of low affinity fragments and also some of the key principles that are used to generate a fragment library. In addition, I describe an example of how FBDD has led to the generation of a drug candidate that is now being tested in clinical trials for the treatment of cancer.

  3. Mass Spectrometry-Based Proteomics in Molecular Diagnostics: Discovery of Cancer Biomarkers Using Tissue Culture

    Directory of Open Access Journals (Sweden)

    Debasish Paul

    2013-01-01

    Full Text Available Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

  4. Mass Spectrometry-Based Proteomics in Molecular Diagnostics: Discovery of Cancer Biomarkers Using Tissue Culture

    Science.gov (United States)

    Paul, Debasish; Kumar, Avinash; Gajbhiye, Akshada; Santra, Manas K.; Srikanth, Rapole

    2013-01-01

    Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches. PMID:23586059

  5. Discovery of pyridine-based agrochemicals by using Intermediate Derivatization Methods.

    Science.gov (United States)

    Guan, Ai-Ying; Liu, Chang-Ling; Sun, Xu-Feng; Xie, Yong; Wang, Ming-An

    2016-02-01

    Pyridine-based compounds have been playing a crucial role as agrochemicals or pesticides including fungicides, insecticides/acaricides and herbicides, etc. Since most of the agrochemicals listed in the Pesticide Manual were discovered through screening programs that relied on trial-and-error testing and new agrochemical discovery is not benefiting as much from the in silico new chemical compound identification/discovery techniques used in pharmaceutical research, it has become more important to find new methods to enhance the efficiency of discovering novel lead compounds in the agrochemical field to shorten the time of research phases in order to meet changing market requirements. In this review, we selected 18 representative known agrochemicals containing a pyridine moiety and extrapolate their discovery from the perspective of Intermediate Derivatization Methods in the hope that this approach will have greater appeal to researchers engaged in the discovery of agrochemicals and/or pharmaceuticals. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Functional principles of registry-based service discovery

    NARCIS (Netherlands)

    Sundramoorthy, V.; Tan, C.; Hartel, P.H.; Hartog, den J.I.; Scholten, J.

    2005-01-01

    As Service Discovery Protocols (SDP) are becoming increasingly important for ubiquitous computing, they must behave according to predefined principles. We present the functional Principles of Service Discovery for robust, registry-based service discovery. A methodology to guarantee adherence to

  7. The mass-action law based algorithm for cost-effective approach for cancer drug discovery and development.

    Science.gov (United States)

    Chou, Ting-Chao

    2011-01-01

    The mass-action law based system analysis via mathematical induction and deduction lead to the generalized theory and algorithm that allows computerized simulation of dose-effect dynamics with small size experiments using a small number of data points in vitro, in animals, and in humans. The median-effect equation of the mass-action law deduced from over 300 mechanism specific-equations has been shown to be the unified theory that serves as the common-link for complicated biomedical systems. After using the median-effect principle as the common denominator, its applications are mechanism-independent, drug unit-independent, and dynamic order-independent; and can be used generally for single drug analysis or for multiple drug combinations in constant-ratio or non-constant ratios. Since the "median" is the common link and universal reference point in biological systems, these general enabling lead to computerized quantitative bio-informatics for econo-green bio-research in broad disciplines. Specific applications of the theory, especially relevant to drug discovery, drug combination, and clinical trials, have been cited or illustrated in terms of algorithms, experimental design and computerized simulation for data analysis. Lessons learned from cancer research during the past fifty years provide a valuable opportunity to reflect, and to improve the conventional divergent approach and to introduce a new convergent avenue, based on the mass-action law principle, for the efficient cancer drug discovery and the low-cost drug development.

  8. Approach to cerebrospinal fluid (CSF) biomarker discovery and evaluation in HIV infection.

    Science.gov (United States)

    Price, Richard W; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena; Smith, Richard D; Jacobs, Jon M; Brown, Joseph N; Gisslen, Magnus

    2013-12-01

    Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previously-defined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

  9. Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

    Energy Technology Data Exchange (ETDEWEB)

    Price, Richard W.; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E.; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena S.; Smith, Richard D.; Jacobs, Jon M.; Brown, Joseph N.; Gisslen, Magnus

    2013-12-13

    Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

  10. Application of mass spectrometry-based proteomics for biomarker discovery in neurological disorders

    Directory of Open Access Journals (Sweden)

    Venugopal Abhilash

    2009-01-01

    Full Text Available Mass spectrometry-based quantitative proteomics has emerged as a powerful approach that has the potential to accelerate biomarker discovery, both for diagnostic as well as therapeutic purposes. Proteomics has traditionally been synonymous with 2D gels but is increasingly shifting to the use of gel-free systems and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS. Quantitative proteomic approaches have already been applied to investigate various neurological disorders, especially in the context of identifying biomarkers from cerebrospinal fluid and serum. This review highlights the scope of different applications of quantitative proteomics in understanding neurological disorders with special emphasis on biomarker discovery.

  11. Analysis student self efficacy in terms of using Discovery Learning model with SAVI approach

    Science.gov (United States)

    Sahara, Rifki; Mardiyana, S., Dewi Retno Sari

    2017-12-01

    Often students are unable to prove their academic achievement optimally according to their abilities. One reason is that they often feel unsure that they are capable of completing the tasks assigned to them. For students, such beliefs are necessary. The term belief has called self efficacy. Self efficacy is not something that has brought about by birth or something with permanent quality of an individual, but is the result of cognitive processes, the meaning one's self efficacy will be stimulated through learning activities. Self efficacy has developed and enhanced by a learning model that can stimulate students to foster confidence in their capabilities. One of them is by using Discovery Learning model with SAVI approach. Discovery Learning model with SAVI approach is one of learning models that involves the active participation of students in exploring and discovering their own knowledge and using it in problem solving by utilizing all the sensory devices they have. This naturalistic qualitative research aims to analyze student self efficacy in terms of use the Discovery Learning model with SAVI approach. The subjects of this study are 30 students focused on eight students who have high, medium, and low self efficacy obtained through purposive sampling technique. The data analysis of this research used three stages, that were reducing, displaying, and getting conclusion of the data. Based on the results of data analysis, it was concluded that the self efficacy appeared dominantly on the learning by using Discovery Learning model with SAVI approach is magnitude dimension.

  12. Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery

    Directory of Open Access Journals (Sweden)

    Yongyao Jiang

    2016-04-01

    Full Text Available Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upon in this paper as a critical step for extracting usage patterns. However, reconstructing user sessions from raw web logs has always been difficult, as a session identifier tends to be missing in most data portals. To address this problem, we propose two session identification methods, including time-clustering-based and time-referrer-based methods. We also present the workflow of session reconstruction and discuss the approach of selecting appropriate thresholds for relevant steps in the workflow. The proposed session identification methods and workflow are proven to be able to extract data access patterns for further pattern analyses of user behavior and improvement of data discovery for more relevancy data ranking, suggestion, and navigation.

  13. NMR approaches in structure-based lead discovery: recent developments and new frontiers for targeting multi-protein complexes.

    Science.gov (United States)

    Dias, David M; Ciulli, Alessio

    2014-01-01

    Nuclear magnetic resonance (NMR) spectroscopy is a pivotal method for structure-based and fragment-based lead discovery because it is one of the most robust techniques to provide information on protein structure, dynamics and interaction at an atomic level in solution. Nowadays, in most ligand screening cascades, NMR-based methods are applied to identify and structurally validate small molecule binding. These can be high-throughput and are often used synergistically with other biophysical assays. Here, we describe current state-of-the-art in the portfolio of available NMR-based experiments that are used to aid early-stage lead discovery. We then focus on multi-protein complexes as targets and how NMR spectroscopy allows studying of interactions within the high molecular weight assemblies that make up a vast fraction of the yet untargeted proteome. Finally, we give our perspective on how currently available methods could build an improved strategy for drug discovery against such challenging targets. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Semantic Search in E-Discovery: An Interdisciplinary Approach

    NARCIS (Netherlands)

    Graus, D.; Ren, Z.; de Rijke, M.; van Dijk, D.; Henseler, H.; van der Knaap, N.

    2013-01-01

    We propose an interdisciplinary approach to applying and evaluating semantic search in the e-discovery setting. By combining expertise from the fields of law and criminology with that of information retrieval and extraction, we move beyond "algorithm-centric" evaluation, towards evaluating the

  15. Discovery of inhibitors of bacterial histidine kinases

    NARCIS (Netherlands)

    Velikova, N.R.

    2014-01-01

    Discovery of Inhibitors of Bacterial Histidine Kinases Summary

    The thesis is on novel antibacterial drug discovery (http://youtu.be/NRMWOGgeysM). Using structure-based and fragment-based drug discovery approach, we have identified small-molecule histidine-kinase

  16. Discovery of potent, reversible MetAP2 inhibitors via fragment based drug discovery and structure based drug design-Part 2.

    Science.gov (United States)

    McBride, Christopher; Cheruvallath, Zacharia; Komandla, Mallareddy; Tang, Mingnam; Farrell, Pamela; Lawson, J David; Vanderpool, Darin; Wu, Yiqin; Dougan, Douglas R; Plonowski, Artur; Holub, Corine; Larson, Chris

    2016-06-15

    Methionine aminopeptidase-2 (MetAP2) is an enzyme that cleaves an N-terminal methionine residue from a number of newly synthesized proteins. This step is required before they will fold or function correctly. Pre-clinical and clinical studies with a MetAP2 inhibitor suggest that they could be used as a novel treatment for obesity. Herein we describe the discovery of a series of pyrazolo[4,3-b]indoles as reversible MetAP2 inhibitors. A fragment-based drug discovery (FBDD) approach was used, beginning with the screening of fragment libraries to generate hits with high ligand-efficiency (LE). An indazole core was selected for further elaboration, guided by structural information. SAR from the indazole series led to the design of a pyrazolo[4,3-b]indole core and accelerated knowledge-based fragment growth resulted in potent and efficient MetAP2 inhibitors, which have shown robust and sustainable body weight loss in DIO mice when dosed orally. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. CLARM: An integrative approach for functional modules discovery

    KAUST Repository

    Salem, Saeed M.; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Brewer, James E.; Aljarah, Ibrahim

    2011-01-01

    Functional module discovery aims to find well-connected subnetworks which can serve as candidate protein complexes. Advances in High-throughput proteomic technologies have enabled the collection of large amount of interaction data as well as gene expression data. We propose, CLARM, a clustering algorithm that integrates gene expression profiles and protein protein interaction network for biological modules discovery. The main premise is that by enriching the interaction network by adding interactions between genes which are highly co-expressed over a wide range of biological and environmental conditions, we can improve the quality of the discovered modules. Protein protein interactions, known protein complexes, and gene expression profiles for diverse environmental conditions from the yeast Saccharomyces cerevisiae were used for evaluate the biological significance of the reported modules. Our experiments show that the CLARM approach is competitive to wellestablished module discovery methods. Copyright © 2011 ACM.

  18. "Structured Discovery": A Modified Inquiry Approach to Teaching Social Studies.

    Science.gov (United States)

    Lordon, John

    1981-01-01

    Describes structured discovery approach to inquiry teaching which encourages the teacher to select instructional objectives, content, and questions to be answered. The focus is on individual and group activities. A brief outline using this approach to analyze Adolf Hitler is presented. (KC)

  19. A Framework for Automatic Web Service Discovery Based on Semantics and NLP Techniques

    Directory of Open Access Journals (Sweden)

    Asma Adala

    2011-01-01

    Full Text Available As a greater number of Web Services are made available today, automatic discovery is recognized as an important task. To promote the automation of service discovery, different semantic languages have been created that allow describing the functionality of services in a machine interpretable form using Semantic Web technologies. The problem is that users do not have intimate knowledge about semantic Web service languages and related toolkits. In this paper, we propose a discovery framework that enables semantic Web service discovery based on keywords written in natural language. We describe a novel approach for automatic discovery of semantic Web services which employs Natural Language Processing techniques to match a user request, expressed in natural language, with a semantic Web service description. Additionally, we present an efficient semantic matching technique to compute the semantic distance between ontological concepts.

  20. Discovery and Development of ATP-Competitive mTOR Inhibitors Using Computational Approaches.

    Science.gov (United States)

    Luo, Yao; Wang, Ling

    2017-11-16

    The mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. This protein is an attractive target for new anticancer drug development. Significant progress has been made in hit discovery, lead optimization, drug candidate development and determination of the three-dimensional (3D) structure of mTOR. Computational methods have been applied to accelerate the discovery and development of mTOR inhibitors helping to model the structure of mTOR, screen compound databases, uncover structure-activity relationship (SAR) and optimize the hits, mine the privileged fragments and design focused libraries. Besides, computational approaches were also applied to study protein-ligand interactions mechanisms and in natural product-driven drug discovery. Herein, we survey the most recent progress on the application of computational approaches to advance the discovery and development of compounds targeting mTOR. Future directions in the discovery of new mTOR inhibitors using computational methods are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Renaissance in Antibiotic Discovery: Some Novel Approaches for Finding Drugs to Treat Bad Bugs.

    Science.gov (United States)

    Gadakh, Bharat; Van Aerschot, Arthur

    2015-01-01

    With the alarming resistance to currently used antibiotics, there is a serious worldwide threat to public health. Therefore, there is an urgent need to search for new antibiotics or new cellular targets which are essential for survival of the pathogens. However, during the past 50 years, only two new classes of antibiotics (oxazolidinone and lipopeptides) have reached the clinic. This suggests that the success rate in discovering new/novel antibiotics using conventional approaches is limited and that we must reconsider our antibiotic discovery approaches. While many new strategies are being pursued lately, this review primarily focuses only on a few of these novel/new approaches for antibiotic discovery. These include structure-based drug design (SBDD), the genomic approach, anti-virulence strategy, targeting nonmultiplying bacteria and the use of bacteriophages. In general, recent advancements in nuclear magnetic resonance, Xcrystallography, and genomic evolution have significant impact on antibacterial drug research. This review therefore aims to discuss recent strategies in searching new antibacterial agents making use of these technical novelties, their advantages, disadvantages and limitations.

  2. Resource Discovery in Activity-Based Sensor Networks

    DEFF Research Database (Denmark)

    Bucur, Doina; Bardram, Jakob

    This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

  3. Rediscovering Don Swanson: The Past, Present and Future of Literature-based Discovery

    Directory of Open Access Journals (Sweden)

    Neil R. Smalheiser

    2017-12-01

    Full Text Available Purpose: The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don’s contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed. Design/methodology/approach: Personal recollections and literature review. Findings: The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses. It continues to be a fertile area of research in a wide range of application areas including text mining, drug repurposing, studies of scientific innovation, knowledge discovery in databases, and bioinformatics. Recently, additional modes of discovery that do not follow the A-B-C model have also been proposed and explored (e.g. so-called storytelling, gaps, analogies, link prediction, negative consensus, outliers, and revival of neglected or discarded research questions. Research limitations: This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery. Practical implications: The general scientific public is still not aware of the availability of tools for literature-based discovery. Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public (http://arrowsmith.psych.uic.edu, as does BITOLA which is maintained by Dmitar Hristovski (http:// http://ibmi.mf.uni-lj.si/bitola, and Epiphanet which is maintained by Trevor Cohen (http://epiphanet.uth.tmc.edu/. Bringing user-friendly tools to the public should be a high priority, since even more than advancing basic research in informatics, it is vital that we ensure that scientists

  4. Semantic Approaches for Knowledge Discovery and Retrieval in Biomedicine

    DEFF Research Database (Denmark)

    Wilkowski, Bartlomiej

    This thesis discusses potential applications of semantics to the recent literaturebased informatics systems to facilitate knowledge discovery, hypothesis generation, and literature retrieval in the domain of biomedicine. The approaches presented herein make use of semantic information extracted...

  5. Two combinatorial optimization problems for SNP discovery using base-specific cleavage and mass spectrometry.

    Science.gov (United States)

    Chen, Xin; Wu, Qiong; Sun, Ruimin; Zhang, Louxin

    2012-01-01

    The discovery of single-nucleotide polymorphisms (SNPs) has important implications in a variety of genetic studies on human diseases and biological functions. One valuable approach proposed for SNP discovery is based on base-specific cleavage and mass spectrometry. However, it is still very challenging to achieve the full potential of this SNP discovery approach. In this study, we formulate two new combinatorial optimization problems. While both problems are aimed at reconstructing the sample sequence that would attain the minimum number of SNPs, they search over different candidate sequence spaces. The first problem, denoted as SNP - MSP, limits its search to sequences whose in silico predicted mass spectra have all their signals contained in the measured mass spectra. In contrast, the second problem, denoted as SNP - MSQ, limits its search to sequences whose in silico predicted mass spectra instead contain all the signals of the measured mass spectra. We present an exact dynamic programming algorithm for solving the SNP - MSP problem and also show that the SNP - MSQ problem is NP-hard by a reduction from a restricted variation of the 3-partition problem. We believe that an efficient solution to either problem above could offer a seamless integration of information in four complementary base-specific cleavage reactions, thereby improving the capability of the underlying biotechnology for sensitive and accurate SNP discovery.

  6. Discovery of resources using MADM approaches for parallel and distributed computing

    Directory of Open Access Journals (Sweden)

    Mandeep Kaur

    2017-06-01

    Full Text Available Grid, a form of parallel and distributed computing, allows the sharing of data and computational resources among its users from various geographical locations. The grid resources are diverse in terms of their underlying attributes. The majority of the state-of-the-art resource discovery techniques rely on the static resource attributes during resource selection. However, the matching resources based on the static resource attributes may not be the most appropriate resources for the execution of user applications because they may have heavy job loads, less storage space or less working memory (RAM. Hence, there is a need to consider the current state of the resources in order to find the most suitable resources. In this paper, we have proposed a two-phased multi-attribute decision making (MADM approach for discovery of grid resources by using P2P formalism. The proposed approach considers multiple resource attributes for decision making of resource selection and provides the best suitable resource(s to grid users. The first phase describes a mechanism to discover all matching resources and applies SAW method to shortlist the top ranked resources, which are communicated to the requesting super-peer. The second phase of our proposed methodology applies integrated MADM approach (AHP enriched PROMETHEE-II on the list of selected resources received from different super-peers. The pairwise comparison of the resources with respect to their attributes is made and the rank of each resource is determined. The top ranked resource is then communicated to the grid user by the grid scheduler. Our proposed methodology enables the grid scheduler to allocate the most suitable resource to the user application and also reduces the search complexity by filtering out the less suitable resources during resource discovery.

  7. A new approach to the rationale discovery of polymeric biomaterials

    Science.gov (United States)

    Kohn, Joachim; Welsh, William J.; Knight, Doyle

    2007-01-01

    This paper attempts to illustrate both the need for new approaches to biomaterials discovery as well as the significant promise inherent in the use of combinatorial and computational design strategies. The key observation of this Leading Opinion Paper is that the biomaterials community has been slow to embrace advanced biomaterials discovery tools such as combinatorial methods, high throughput experimentation, and computational modeling in spite of the significant promise shown by these discovery tools in materials science, medicinal chemistry and the pharmaceutical industry. It seems that the complexity of living cells and their interactions with biomaterials has been a conceptual as well as a practical barrier to the use of advanced discovery tools in biomaterials science. However, with the continued increase in computer power, the goal of predicting the biological response of cells in contact with biomaterials surfaces is within reach. Once combinatorial synthesis, high throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems. PMID:17644176

  8. In silico tools used for compound selection during target-based drug discovery and development.

    Science.gov (United States)

    Caldwell, Gary W

    2015-01-01

    The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies. This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach. There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.

  9. Using concepts in literature-based discovery : Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries

    NARCIS (Netherlands)

    Weeber, M; Klein, Henny; de Jong-van den Berg, LTW; Vos, R

    Literature-based discovery has resulted in new knowledge. In the biomedical context, Don R. Swanson has generated several literature-based hypotheses that have been corroborated experimentally and clinically. In this paper, we propose a two-step model of the discovery process in which hypotheses are

  10. Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.

    Science.gov (United States)

    Heifetz, Alexander; Southey, Michelle; Morao, Inaki; Townsend-Nicholson, Andrea; Bodkin, Mike J

    2018-01-01

    GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

  11. Sports Stars: Analyzing the Performance of Astronomers at Visualization-based Discovery

    Science.gov (United States)

    Fluke, C. J.; Parrington, L.; Hegarty, S.; MacMahon, C.; Morgan, S.; Hassan, A. H.; Kilborn, V. A.

    2017-05-01

    In this data-rich era of astronomy, there is a growing reliance on automated techniques to discover new knowledge. The role of the astronomer may change from being a discoverer to being a confirmer. But what do astronomers actually look at when they distinguish between “sources” and “noise?” What are the differences between novice and expert astronomers when it comes to visual-based discovery? Can we identify elite talent or coach astronomers to maximize their potential for discovery? By looking to the field of sports performance analysis, we consider an established, domain-wide approach, where the expertise of the viewer (i.e., a member of the coaching team) plays a crucial role in identifying and determining the subtle features of gameplay that provide a winning advantage. As an initial case study, we investigate whether the SportsCode performance analysis software can be used to understand and document how an experienced Hi astronomer makes discoveries in spectral data cubes. We find that the process of timeline-based coding can be applied to spectral cube data by mapping spectral channels to frames within a movie. SportsCode provides a range of easy to use methods for annotation, including feature-based codes and labels, text annotations associated with codes, and image-based drawing. The outputs, including instance movies that are uniquely associated with coded events, provide the basis for a training program or team-based analysis that could be used in unison with discipline specific analysis software. In this coordinated approach to visualization and analysis, SportsCode can act as a visual notebook, recording the insight and decisions in partnership with established analysis methods. Alternatively, in situ annotation and coding of features would be a valuable addition to existing and future visualization and analysis packages.

  12. 3D profile-based approach to proteome-wide discovery of novel human chemokines.

    Directory of Open Access Journals (Sweden)

    Aurelie Tomczak

    Full Text Available Chemokines are small secreted proteins with important roles in immune responses. They consist of a conserved three-dimensional (3D structure, so-called IL8-like chemokine fold, which is supported by disulfide bridges characteristic of this protein family. Sequence- and profile-based computational methods have been proficient in discovering novel chemokines by making use of their sequence-conserved cysteine patterns. However, it has been recently shown that some chemokines escaped annotation by these methods due to low sequence similarity to known chemokines and to different arrangement of cysteines in sequence and in 3D. Innovative methods overcoming the limitations of current techniques may allow the discovery of new remote homologs in the still functionally uncharacterized fraction of the human genome. We report a novel computational approach for proteome-wide identification of remote homologs of the chemokine family that uses fold recognition techniques in combination with a scaffold-based automatic mapping of disulfide bonds to define a 3D profile of the chemokine protein family. By applying our methodology to all currently uncharacterized human protein sequences, we have discovered two novel proteins that, without having significant sequence similarity to known chemokines or characteristic cysteine patterns, show strong structural resemblance to known anti-HIV chemokines. Detailed computational analysis and experimental structural investigations based on mass spectrometry and circular dichroism support our structural predictions and highlight several other chemokine-like features. The results obtained support their functional annotation as putative novel chemokines and encourage further experimental characterization. The identification of remote homologs of human chemokines may provide new insights into the molecular mechanisms causing pathologies such as cancer or AIDS, and may contribute to the development of novel treatments. Besides

  13. DISCOVERY LEARNING APPROACH IN IMPROVING ARABIC ABILITY OF PRE-SERVICE TEACHERS IN RELIGIOUS TRAINING CENTRE OF MAKASSAR

    Directory of Open Access Journals (Sweden)

    Masrariah Amin

    2017-04-01

    Full Text Available Discovery Learning can be defined as the learning that takes place when the student is not presented with subject matter in the final form, rather he/she is required to find out the concepts by him/her self. This research aims to describe and analyze discovery learning method to strategically improve the comprehension and reasoning ability of Arabic pre-service teachers, which can motivate and enhance their creativity in order to enrich their insight about Arabic teaching as well, especially those who are in training centre. This research was undertaken in two classes of Makassar Religious Training Centre during June-August 2016. The design of this research is experiment with discovery learning approach with randomized pretest-posttest control group design. It was done randomly when to choosing the participants to be experiment and control group. Based on hypothesis testing, discovery learning has positive effects on the pre-service teachers’ Arabic ability in training centre to understand and analyze Arabic. Therefore, based on two-variance analysis; control and experiment group, there is difference on teachers’ comprehension and reasoning ability in learning Arabic between experiment and control group by using discovery learning and conventional method.

  14. Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases.

    Science.gov (United States)

    Mello, Juliana da Fonseca Rezende E; Gomes, Renan Augusto; Vital-Fujii, Drielli Gomes; Ferreira, Glaucio Monteiro; Trossini, Gustavo Henrique Goulart

    2017-12-01

    Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs. © 2017 John Wiley & Sons A/S.

  15. Gene set-based module discovery in the breast cancer transcriptome

    Directory of Open Access Journals (Sweden)

    Zhang Michael Q

    2009-02-01

    Full Text Available Abstract Background Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data. Results In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2 is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells. Conclusion These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.

  16. Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

    Science.gov (United States)

    Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

    2010-01-18

    The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

  17. Combining NMR and X-ray crystallography in fragment-based drug discovery: discovery of highly potent and selective BACE-1 inhibitors.

    Science.gov (United States)

    Wyss, Daniel F; Wang, Yu-Sen; Eaton, Hugh L; Strickland, Corey; Voigt, Johannes H; Zhu, Zhaoning; Stamford, Andrew W

    2012-01-01

    Fragment-based drug discovery (FBDD) has become increasingly popular over the last decade. We review here how we have used highly structure-driven fragment-based approaches to complement more traditional lead discovery to tackle high priority targets and those struggling for leads. Combining biomolecular nuclear magnetic resonance (NMR), X-ray crystallography, and molecular modeling with structure-assisted chemistry and innovative biology as an integrated approach for FBDD can solve very difficult problems, as illustrated in this chapter. Here, a successful FBDD campaign is described that has allowed the development of a clinical candidate for BACE-1, a challenging CNS drug target. Crucial to this achievement were the initial identification of a ligand-efficient isothiourea fragment through target-based NMR screening and the determination of its X-ray crystal structure in complex with BACE-1, which revealed an extensive H-bond network with the two active site aspartate residues. This detailed 3D structural information then enabled the design and validation of novel, chemically stable and accessible heterocyclic acylguanidines as aspartic acid protease inhibitor cores. Structure-assisted fragment hit-to-lead optimization yielded iminoheterocyclic BACE-1 inhibitors that possess desirable molecular properties as potential therapeutic agents to test the amyloid hypothesis of Alzheimer's disease in a clinical setting.

  18. Fragment Based Strategies for Discovery of Novel HIV-1 Reverse Transcriptase and Integrase Inhibitors.

    Science.gov (United States)

    Latham, Catherine F; La, Jennifer; Tinetti, Ricky N; Chalmers, David K; Tachedjian, Gilda

    2016-01-01

    Human immunodeficiency virus (HIV) remains a global health problem. While combined antiretroviral therapy has been successful in controlling the virus in patients, HIV can develop resistance to drugs used for treatment, rendering available drugs less effective and limiting treatment options. Initiatives to find novel drugs for HIV treatment are ongoing, although traditional drug design approaches often focus on known binding sites for inhibition of established drug targets like reverse transcriptase and integrase. These approaches tend towards generating more inhibitors in the same drug classes already used in the clinic. Lack of diversity in antiretroviral drug classes can result in limited treatment options, as cross-resistance can emerge to a whole drug class in patients treated with only one drug from that class. A fresh approach in the search for new HIV-1 drugs is fragment-based drug discovery (FBDD), a validated strategy for drug discovery based on using smaller libraries of low molecular weight molecules (FBDD is aimed at not only finding novel drug scaffolds, but also probing the target protein to find new, often allosteric, inhibitory binding sites. Several fragment-based strategies have been successful in identifying novel inhibitory sites or scaffolds for two proven drug targets for HIV-1, reverse transcriptase and integrase. While any FBDD-generated HIV-1 drugs have yet to enter the clinic, recent FBDD initiatives against these two well-characterised HIV-1 targets have reinvigorated antiretroviral drug discovery and the search for novel classes of HIV-1 drugs.

  19. Fragment-based drug discovery and its application to challenging drug targets.

    Science.gov (United States)

    Price, Amanda J; Howard, Steven; Cons, Benjamin D

    2017-11-08

    Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein-fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein-protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

  20. Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

    Science.gov (United States)

    Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

    2017-10-01

    While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

  1. Rule Induction-Based Knowledge Discovery for Energy Efficiency

    OpenAIRE

    Chen, Qipeng; Fan, Zhong; Kaleshi, Dritan; Armour, Simon M D

    2015-01-01

    Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induct...

  2. Fighting obesity with a sugar-based library: discovery of novel MCH-1R antagonists by a new computational-VAST approach for exploration of GPCR binding sites.

    Science.gov (United States)

    Heifetz, Alexander; Barker, Oliver; Verquin, Geraldine; Wimmer, Norbert; Meutermans, Wim; Pal, Sandeep; Law, Richard J; Whittaker, Mark

    2013-05-24

    Obesity is an increasingly common disease. While antagonism of the melanin-concentrating hormone-1 receptor (MCH-1R) has been widely reported as a promising therapeutic avenue for obesity treatment, no MCH-1R antagonists have reached the market. Discovery and optimization of new chemical matter targeting MCH-1R is hindered by reduced HTS success rates and a lack of structural information about the MCH-1R binding site. X-ray crystallography and NMR, the major experimental sources of structural information, are very slow processes for membrane proteins and are not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of these methods to impact the drug discovery process for GPCR targets in "real-time", and hence, there is an urgent need for other practical and cost-efficient alternatives. We present here a conceptually pioneering approach that integrates GPCR modeling with design, synthesis, and screening of a diverse library of sugar-based compounds from the VAST technology (versatile assembly on stable templates) to provide structural insights on the MCH-1R binding site. This approach creates a cost-efficient new avenue for structure-based drug discovery (SBDD) against GPCR targets. In our work, a primary VAST hit was used to construct a high-quality MCH-1R model. Following model validation, a structure-based virtual screen yielded a 14% hit rate and 10 novel chemotypes of potent MCH-1R antagonists, including EOAI3367472 (IC50 = 131 nM) and EOAI3367474 (IC50 = 213 nM).

  3. The Genetics of Obsessive-Compulsive Disorder and Tourette Syndrome: An Epidemiological and Pathway-Based Approach for Gene Discovery

    Science.gov (United States)

    Grados, Marco A.

    2010-01-01

    Objective: To provide a contemporary perspective on genetic discovery methods applied to obsessive-compulsive disorder (OCD) and Tourette syndrome (TS). Method: A review of research trends in genetics research in OCD and TS is conducted, with emphasis on novel approaches. Results: Genome-wide association studies (GWAS) are now in progress in OCD…

  4. The rise of fragment-based drug discovery.

    Science.gov (United States)

    Murray, Christopher W; Rees, David C

    2009-06-01

    The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (∼150 Da) that bind to biologically important macromolecules. The three-dimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug-like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns. Here, we review the rise of FBDD, including its application to discovering clinical candidates against targets for which other chemistry approaches have struggled.

  5. Fragment-Based Drug Discovery in the Bromodomain and Extra-Terminal Domain Family.

    Science.gov (United States)

    Radwan, Mostafa; Serya, Rabah

    2017-08-01

    Bromodomain and extra-terminal domain (BET) inhibition has emerged recently as a potential therapeutic target for the treatment of many human disorders such as atherosclerosis, inflammatory disorders, chronic obstructive pulmonary disease (COPD), some viral infections, and cancer. Since the discovery of the two potent inhibitors, I-BET762 and JQ1, different research groups have used different techniques to develop novel potent and selective inhibitors. In this review, we will be concerned with the trials that used fragment-based drug discovery (FBDD) approaches to discover or optimize BET inhibitors, also showing fragments that can be further optimized in future projects to reach novel potent BET inhibitors. © 2017 Deutsche Pharmazeutische Gesellschaft.

  6. Harvest: a web-based biomedical data discovery and reporting application development platform.

    Science.gov (United States)

    Italia, Michael J; Pennington, Jeffrey W; Ruth, Byron; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; Miller, Jeffrey; White, Peter S

    2013-01-01

    Biomedical researchers share a common challenge of making complex data understandable and accessible. This need is increasingly acute as investigators seek opportunities for discovery amidst an exponential growth in the volume and complexity of laboratory and clinical data. To address this need, we developed Harvest, an open source framework that provides a set of modular components to aid the rapid development and deployment of custom data discovery software applications. Harvest incorporates visual representations of multidimensional data types in an intuitive, web-based interface that promotes a real-time, iterative approach to exploring complex clinical and experimental data. The Harvest architecture capitalizes on standards-based, open source technologies to address multiple functional needs critical to a research and development environment, including domain-specific data modeling, abstraction of complex data models, and a customizable web client.

  7. A systematic approach to novel virus discovery in emerging infectious disease outbreaks.

    Science.gov (United States)

    Sridhar, Siddharth; To, Kelvin K W; Chan, Jasper F W; Lau, Susanna K P; Woo, Patrick C Y; Yuen, Kwok-Yung

    2015-05-01

    The discovery of novel viruses is of great importance to human health-both in the setting of emerging infectious disease outbreaks and in disease syndromes of unknown etiology. Despite the recent proliferation of many efficient virus discovery methods, careful selection of a combination of methods is important to demonstrate a novel virus, its clinical associations, and its relevance in a timely manner. The identification of a patient or an outbreak with distinctive clinical features and negative routine microbiological workup is often the starting point for virus hunting. This review appraises the roles of culture, electron microscopy, and nucleic acid detection-based methods in optimizing virus discovery. Cell culture is generally slow but may yield viable virus. Although the choice of cell line often involves trial and error, it may be guided by the clinical syndrome. Electron microscopy is insensitive but fast, and may provide morphological clues to choice of cell line or consensus primers for nucleic acid detection. Consensus primer PCR can be used to detect viruses that are closely related to known virus families. Random primer amplification and high-throughput sequencing can catch any virus genome but cannot yield an infectious virion for testing Koch postulates. A systematic approach that incorporates carefully chosen combinations of virus detection techniques is required for successful virus discovery. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

  8. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery.

    Science.gov (United States)

    Janga, Sarath Chandra; Tzakos, Andreas

    2009-12-01

    Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.

  9. Network-based Approaches in Pharmacology.

    Science.gov (United States)

    Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

    2017-10-01

    In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Computational medicinal chemistry in fragment-based drug discovery: what, how and when.

    Science.gov (United States)

    Rabal, Obdulia; Urbano-Cuadrado, Manuel; Oyarzabal, Julen

    2011-01-01

    The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

  11. Cloud computing approaches to accelerate drug discovery value chain.

    Science.gov (United States)

    Garg, Vibhav; Arora, Suchir; Gupta, Chitra

    2011-12-01

    Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

  12. Pharmacological screening technologies for venom peptide discovery.

    Science.gov (United States)

    Prashanth, Jutty Rajan; Hasaballah, Nojod; Vetter, Irina

    2017-12-01

    Venomous animals occupy one of the most successful evolutionary niches and occur on nearly every continent. They deliver venoms via biting and stinging apparatuses with the aim to rapidly incapacitate prey and deter predators. This has led to the evolution of venom components that act at a number of biological targets - including ion channels, G-protein coupled receptors, transporters and enzymes - with exquisite selectivity and potency, making venom-derived components attractive pharmacological tool compounds and drug leads. In recent years, plate-based pharmacological screening approaches have been introduced to accelerate venom-derived drug discovery. A range of assays are amenable to this purpose, including high-throughput electrophysiology, fluorescence-based functional and binding assays. However, despite these technological advances, the traditional activity-guided fractionation approach is time-consuming and resource-intensive. The combination of screening techniques suitable for miniaturization with sequence-based discovery approaches - supported by advanced proteomics, mass spectrometry, chromatography as well as synthesis and expression techniques - promises to further improve venom peptide discovery. Here, we discuss practical aspects of establishing a pipeline for venom peptide drug discovery with a particular emphasis on pharmacology and pharmacological screening approaches. This article is part of the Special Issue entitled 'Venom-derived Peptides as Pharmacological Tools.' Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

    Science.gov (United States)

    Seko, Atsuto; Hayashi, Hiroyuki; Kashima, Hisashi; Tanaka, Isao

    2018-01-01

    Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine learning is a unique approach to search for currently unknown CRCs from vast candidates. Herein we propose matrix- and tensor-based recommender system approaches to predict currently unknown CRCs from database entries of CRCs. Firstly, the performance of the recommender system approaches to discover currently unknown CRCs is examined. A Tucker decomposition recommender system shows the best discovery rate of CRCs as the majority of the top 100 recommended ternary and quaternary compositions correspond to CRCs. Secondly, systematic density functional theory (DFT) calculations are performed to investigate the phase stability of the recommended compositions. The phase stability of the 27 compositions reveals that 23 currently unknown compounds are newly found to be stable. These results indicate that the recommender system has great potential to accelerate the discovery of new compounds.

  14. Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

    Energy Technology Data Exchange (ETDEWEB)

    Çakır, Tunahan, E-mail: tcakir@gyte.edu.tr [Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey); Khatibipour, Mohammad Jafar [Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey); Department of Chemical Engineering, Gebze Technical University (formerly known as Gebze Institute of Technology), Gebze (Turkey)

    2014-12-03

    The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

  15. Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

    International Nuclear Information System (INIS)

    Çakır, Tunahan; Khatibipour, Mohammad Jafar

    2014-01-01

    The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

  16. Mass Spectrometry-Based Biomarker Discovery.

    Science.gov (United States)

    Zhou, Weidong; Petricoin, Emanuel F; Longo, Caterina

    2017-01-01

    The discovery of candidate biomarkers within the entire proteome is one of the most important and challenging goals in proteomic research. Mass spectrometry-based proteomics is a modern and promising technology for semiquantitative and qualitative assessment of proteins, enabling protein sequencing and identification with exquisite accuracy and sensitivity. For mass spectrometry analysis, protein extractions from tissues or body fluids and subsequent protein fractionation represent an important and unavoidable step in the workflow for biomarker discovery. Following extraction of proteins, the protein mixture must be digested, reduced, alkylated, and cleaned up prior to mass spectrometry. The aim of our chapter is to provide comprehensible and practical lab procedures for sample digestion, protein fractionation, and subsequent mass spectrometry analysis.

  17. Representation Discovery using Harmonic Analysis

    CERN Document Server

    Mahadevan, Sridhar

    2008-01-01

    Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particu

  18. Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

    Science.gov (United States)

    Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

    2016-08-01

    The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

  19. A wavelet-based approach to the discovery of themes and sections in monophonic melodies

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David

    We present the computational method submitted to the MIREX 2014 Discovery of Repeated Themes & Sections task, and the results on the monophonic version of the JKU Patterns Development Database. In the context of pattern discovery in monophonic music, the idea behind our method is that, with a good...

  20. A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery.

    Science.gov (United States)

    Hellerstein, Marc K

    2008-01-01

    Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.

  1. EFEKTIVITAS PENDEKATAN SAINTIFIK BERBASIS GROUP INVESTIGATION DAN DISCOVERY LEARNING DITINJAU DARI MINAT BELAJAR MAHASISWA

    Directory of Open Access Journals (Sweden)

    Ira Vahlia

    2017-06-01

    Full Text Available Appropriate learning models contribute to student learning interest in math. The purpose of this study is to describe the difference in effectiveness between scientific approach based on group investigation and discovery in terms of student's interest in learning. The research that is conducted is quasi experimental research and the design used is 2 x factorial description. In experimental class I that apply scientific approach model based on study group investigation obtained the average value of learning outcome of 66.60 while in the experimental class II applying the approach Science-based discovery learning obtained the average value of posttest of 76.28. Based on the marginal rate, the scientific approach to discovery-based learning on moderate interest in learning outcomes is higher than the learning outcomes at high and low interest. In the scientific approach based on group study investigation and discovery learning there are differences in average learning outcomes between high, medium and low interest. Scientific approach based on group investigation learning on higher interest in learning outcomes is higher than moderate and low interest.

  2. A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents.

    Directory of Open Access Journals (Sweden)

    Yunierkis Perez-Castillo

    Full Text Available Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.

  3. Understanding price discovery in interconnected markets: Generalized Langevin process approach and simulation

    Science.gov (United States)

    Schenck, Natalya A.; Horvath, Philip A.; Sinha, Amit K.

    2018-02-01

    While the literature on price discovery process and information flow between dominant and satellite market is exhaustive, most studies have applied an approach that can be traced back to Hasbrouck (1995) or Gonzalo and Granger (1995). In this paper, however, we propose a Generalized Langevin process with asymmetric double-well potential function, with co-integrated time series and interconnected diffusion processes to model the information flow and price discovery process in two, a dominant and a satellite, interconnected markets. A simulated illustration of the model is also provided.

  4. The current state of GPCR-based drug discovery to treat metabolic disease.

    Science.gov (United States)

    Sloop, Kyle W; Emmerson, Paul J; Statnick, Michael A; Willard, Francis S

    2018-02-02

    One approach of modern drug discovery is to identify agents that enhance or diminish signal transduction cascades in various cell types and tissues by modulating the activity of GPCRs. This strategy has resulted in the development of new medicines to treat many conditions, including cardiovascular disease, psychiatric disorders, HIV/AIDS, certain forms of cancer and Type 2 diabetes mellitus (T2DM). These successes justify further pursuit of GPCRs as disease targets and provide key learning that should help guide identifying future therapeutic agents. This report reviews the current landscape of GPCR drug discovery with emphasis on efforts aimed at developing new molecules for treating T2DM and obesity. We analyse historical efforts to generate GPCR-based drugs to treat metabolic disease in terms of causal factors leading to success and failure in this endeavour. © 2018 The British Pharmacological Society.

  5. Discovery of the leinamycin family of natural products by mining actinobacterial genomes.

    Science.gov (United States)

    Pan, Guohui; Xu, Zhengren; Guo, Zhikai; Hindra; Ma, Ming; Yang, Dong; Zhou, Hao; Gansemans, Yannick; Zhu, Xiangcheng; Huang, Yong; Zhao, Li-Xing; Jiang, Yi; Cheng, Jinhua; Van Nieuwerburgh, Filip; Suh, Joo-Won; Duan, Yanwen; Shen, Ben

    2017-12-26

    Nature's ability to generate diverse natural products from simple building blocks has inspired combinatorial biosynthesis. The knowledge-based approach to combinatorial biosynthesis has allowed the production of designer analogs by rational metabolic pathway engineering. While successful, structural alterations are limited, with designer analogs often produced in compromised titers. The discovery-based approach to combinatorial biosynthesis complements the knowledge-based approach by exploring the vast combinatorial biosynthesis repertoire found in Nature. Here we showcase the discovery-based approach to combinatorial biosynthesis by targeting the domain of unknown function and cysteine lyase domain (DUF-SH) didomain, specific for sulfur incorporation from the leinamycin (LNM) biosynthetic machinery, to discover the LNM family of natural products. By mining bacterial genomes from public databases and the actinomycetes strain collection at The Scripps Research Institute, we discovered 49 potential producers that could be grouped into 18 distinct clades based on phylogenetic analysis of the DUF-SH didomains. Further analysis of the representative genomes from each of the clades identified 28 lnm -type gene clusters. Structural diversities encoded by the LNM-type biosynthetic machineries were predicted based on bioinformatics and confirmed by in vitro characterization of selected adenylation proteins and isolation and structural elucidation of the guangnanmycins and weishanmycins. These findings demonstrate the power of the discovery-based approach to combinatorial biosynthesis for natural product discovery and structural diversity and highlight Nature's rich biosynthetic repertoire. Comparative analysis of the LNM-type biosynthetic machineries provides outstanding opportunities to dissect Nature's biosynthetic strategies and apply these findings to combinatorial biosynthesis for natural product discovery and structural diversity.

  6. The application of mass-spectrometry-based protein biomarker discovery to theragnostics

    OpenAIRE

    Street, Jonathan M; Dear, James W

    2010-01-01

    Over the last decade rapid developments in mass spectrometry have allowed the identification of multiple proteins in complex biological samples. This proteomic approach has been applied to biomarker discovery in the context of clinical pharmacology (the combination of biomarker and drug now being termed ‘theragnostics’). In this review we provide a roadmap for early protein biomarker discovery studies, focusing on some key questions that regularly confront researchers.

  7. Combinatorial pattern discovery approach for the folding trajectory analysis of a beta-hairpin.

    Directory of Open Access Journals (Sweden)

    Laxmi Parida

    2005-06-01

    Full Text Available The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters-each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity c in RO((N + nm log n, where N is the size of the output patterns and (n x m is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a beta-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1 The method recovers states previously obtained by visually analyzing free energy surfaces. (2 It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the beta-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3 The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the

  8. Combinatorial Pattern Discovery Approach for the Folding Trajectory Analysis of a beta-Hairpin.

    Directory of Open Access Journals (Sweden)

    2005-06-01

    Full Text Available The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters-each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity cinRO((N + nm log n, where N is the size of the output patterns and (n x m is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a beta-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1 The method recovers states previously obtained by visually analyzing free energy surfaces. (2 It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the beta-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3 The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the

  9. Developing a distributed HTML5-based search engine for geospatial resource discovery

    Science.gov (United States)

    ZHOU, N.; XIA, J.; Nebert, D.; Yang, C.; Gui, Z.; Liu, K.

    2013-12-01

    With explosive growth of data, Geospatial Cyberinfrastructure(GCI) components are developed to manage geospatial resources, such as data discovery and data publishing. However, the efficiency of geospatial resources discovery is still challenging in that: (1) existing GCIs are usually developed for users of specific domains. Users may have to visit a number of GCIs to find appropriate resources; (2) The complexity of decentralized network environment usually results in slow response and pool user experience; (3) Users who use different browsers and devices may have very different user experiences because of the diversity of front-end platforms (e.g. Silverlight, Flash or HTML). To address these issues, we developed a distributed and HTML5-based search engine. Specifically, (1)the search engine adopts a brokering approach to retrieve geospatial metadata from various and distributed GCIs; (2) the asynchronous record retrieval mode enhances the search performance and user interactivity; (3) the search engine based on HTML5 is able to provide unified access capabilities for users with different devices (e.g. tablet and smartphone).

  10. Cell and small animal models for phenotypic drug discovery

    Directory of Open Access Journals (Sweden)

    Szabo M

    2017-06-01

    Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug

  11. Mass spectrometric based approaches in urine metabolomics and biomarker discovery.

    Science.gov (United States)

    Khamis, Mona M; Adamko, Darryl J; El-Aneed, Anas

    2017-03-01

    Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing

  12. Antibody informatics for drug discovery

    DEFF Research Database (Denmark)

    Shirai, Hiroki; Prades, Catherine; Vita, Randi

    2014-01-01

    to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic...... infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies...... for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii...

  13. Discovery of a Novel Inhibitor of the Hedgehog Signaling Pathway through Cell-based Compound Discovery and Target Prediction.

    Science.gov (United States)

    Kremer, Lea; Schultz-Fademrecht, Carsten; Baumann, Matthias; Habenberger, Peter; Choidas, Axel; Klebl, Bert; Kordes, Susanne; Schöler, Hans R; Sterneckert, Jared; Ziegler, Slava; Schneider, Gisbert; Waldmann, Herbert

    2017-10-09

    Cell-based assays enable monitoring of small-molecule bioactivity in a target-agnostic manner and help uncover new biological mechanisms. Subsequent identification and validation of the small-molecule targets, typically employing proteomics techniques, is very challenging and limited, in particular if the targets are membrane proteins. Herein, we demonstrate that the combination of cell-based bioactive-compound discovery with cheminformatic target prediction may provide an efficient approach to accelerate the process and render target identification and validation more efficient. Using a cell-based assay, we identified the pyrazolo-imidazole smoothib as a new inhibitor of hedgehog (Hh) signaling and an antagonist of the protein smoothened (SMO) with a novel chemotype. Smoothib targets the heptahelical bundle of SMO, prevents its ciliary localization, reduces the expression of Hh target genes, and suppresses the growth of Ptch +/- medulloblastoma cells. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Optogenetic Approaches to Drug Discovery in Neuroscience and Beyond.

    Science.gov (United States)

    Zhang, Hongkang; Cohen, Adam E

    2017-07-01

    Recent advances in optogenetics have opened new routes to drug discovery, particularly in neuroscience. Physiological cellular assays probe functional phenotypes that connect genomic data to patient health. Optogenetic tools, in particular tools for all-optical electrophysiology, now provide a means to probe cellular disease models with unprecedented throughput and information content. These techniques promise to identify functional phenotypes associated with disease states and to identify compounds that improve cellular function regardless of whether the compound acts directly on a target or through a bypass mechanism. This review discusses opportunities and unresolved challenges in applying optogenetic techniques throughout the discovery pipeline - from target identification and validation, to target-based and phenotypic screens, to clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Arrayed antibody library technology for therapeutic biologic discovery.

    Science.gov (United States)

    Bentley, Cornelia A; Bazirgan, Omar A; Graziano, James J; Holmes, Evan M; Smider, Vaughn V

    2013-03-15

    Traditional immunization and display antibody discovery methods rely on competitive selection amongst a pool of antibodies to identify a lead. While this approach has led to many successful therapeutic antibodies, targets have been limited to proteins which are easily purified. In addition, selection driven discovery has produced a narrow range of antibody functionalities focused on high affinity antagonism. We review the current progress in developing arrayed protein libraries for screening-based, rather than selection-based, discovery. These single molecule per microtiter well libraries have been screened in multiplex formats against both purified antigens and directly against targets expressed on the cell surface. This facilitates the discovery of antibodies against therapeutically interesting targets (GPCRs, ion channels, and other multispanning membrane proteins) and epitopes that have been considered poorly accessible to conventional discovery methods. Copyright © 2013. Published by Elsevier Inc.

  16. Assessment of Dengue virus helicase and methyltransferase as targets for fragment-based drug discovery.

    Science.gov (United States)

    Coutard, Bruno; Decroly, Etienne; Li, Changqing; Sharff, Andrew; Lescar, Julien; Bricogne, Gérard; Barral, Karine

    2014-06-01

    Seasonal and pandemic flaviviruses continue to be leading global health concerns. With the view to help drug discovery against Dengue virus (DENV), a fragment-based experimental approach was applied to identify small molecule ligands targeting two main components of the flavivirus replication complex: the NS3 helicase (Hel) and the NS5 mRNA methyltransferase (MTase) domains. A library of 500 drug-like fragments was first screened by thermal-shift assay (TSA) leading to the identification of 36 and 32 fragment hits binding Hel and MTase from DENV, respectively. In a second stage, we set up a fragment-based X-ray crystallographic screening (FBS-X) in order to provide both validated fragment hits and structural binding information. No fragment hit was confirmed for DENV Hel. In contrast, a total of seven fragments were identified as DENV MTase binders and structures of MTase-fragment hit complexes were solved at resolution at least 2.0Å or better. All fragment hits identified contain either a five- or six-membered aromatic ring or both, and three novel binding sites were located on the MTase. To further characterize the fragment hits identified by TSA and FBS-X, we performed enzymatic assays to assess their inhibition effect on the N7- and 2'-O-MTase enzymatic activities: five of these fragment hits inhibit at least one of the two activities with IC50 ranging from 180μM to 9mM. This work validates the FBS-X strategy for identifying new anti-flaviviral hits targeting MTase, while Hel might not be an amenable target for fragment-based drug discovery (FBDD). This approach proved to be a fast and efficient screening method for FBDD target validation and discovery of starting hits for the development of higher affinity molecules that bind to novel allosteric sites. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Canonical correlation analysis for gene-based pleiotropy discovery.

    Directory of Open Access Journals (Sweden)

    Jose A Seoane

    2014-10-01

    Full Text Available Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy and for testing multiple variants for association with a single phenotype (gene-based association tests. Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study, we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1 with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.

  18. Current perspectives in fragment-based lead discovery (FBLD)

    Science.gov (United States)

    Lamoree, Bas; Hubbard, Roderick E.

    2017-01-01

    It is over 20 years since the first fragment-based discovery projects were disclosed. The methods are now mature for most ‘conventional’ targets in drug discovery such as enzymes (kinases and proteases) but there has also been growing success on more challenging targets, such as disruption of protein–protein interactions. The main application is to identify tractable chemical startpoints that non-covalently modulate the activity of a biological molecule. In this essay, we overview current practice in the methods and discuss how they have had an impact in lead discovery – generating a large number of fragment-derived compounds that are in clinical trials and two medicines treating patients. In addition, we discuss some of the more recent applications of the methods in chemical biology – providing chemical tools to investigate biological molecules, mechanisms and systems. PMID:29118093

  19. Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

    Science.gov (United States)

    Benson, Neil

    2015-08-01

    Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Computer-Aided Drug Discovery in Plant Pathology.

    Science.gov (United States)

    Shanmugam, Gnanendra; Jeon, Junhyun

    2017-12-01

    Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides .

  1. Effectiveness of Discovery Learning-Based Transformation Geometry Module

    Science.gov (United States)

    Febriana, R.; Haryono, Y.; Yusri, R.

    2017-09-01

    Development of transformation geometry module is conducted because the students got difficulties to understand the existing book. The purpose of the research was to find out the effectiveness of discovery learning-based transformation geometry module toward student’s activity. Model of the development was Plomp model consisting preliminary research, prototyping phase and assessment phase. The research was focused on assessment phase where it was to observe the designed product effectiveness. The instrument was observation sheet. The observed activities were visual activities, oral activities, listening activities, mental activities, emotional activities and motor activities. Based on the result of the research, it is found that visual activities, learning activities, writing activities, the student’s activity is in the criteria very effective. It can be concluded that the use of discovery learning-based transformation geometry module use can increase the positive student’s activity and decrease the negative activity.

  2. Wide-Area Publish/Subscribe Mobile Resource Discovery Based on IPv6 GeoNetworking

    OpenAIRE

    Noguchi, Satoru; Matsuura, Satoshi; Inomata, Atsuo; Fujikawa, Kazutoshi; Sunahara, Hideki

    2013-01-01

    Resource discovery is an essential function for distributed mobile applications integrated in vehicular communication systems. Key requirements of the mobile resource discovery are wide-area geographic-based discovery and scalable resource discovery not only inside a vehicular ad-hoc network but also through the Internet. While a number of resource discovery solutions have been proposed, most of them have focused on specific scale of network. Furthermore, managing a large number of mobile res...

  3. Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.

    Science.gov (United States)

    Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu

    2018-03-01

    Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.

  4. Leveraging the contribution of thermodynamics in drug discovery with the help of fluorescence-based thermal shift assays.

    Science.gov (United States)

    Hau, Jean Christophe; Fontana, Patrizia; Zimmermann, Catherine; De Pover, Alain; Erdmann, Dirk; Chène, Patrick

    2011-06-01

    The development of new drugs with better pharmacological and safety properties mandates the optimization of several parameters. Today, potency is often used as the sole biochemical parameter to identify and select new molecules. Surprisingly, thermodynamics, which is at the core of any interaction, is rarely used in drug discovery, even though it has been suggested that the selection of scaffolds according to thermodynamic criteria may be a valuable strategy. This poor integration of thermodynamics in drug discovery might be due to difficulties in implementing calorimetry experiments despite recent technological progress in this area. In this report, the authors show that fluorescence-based thermal shift assays could be used as prescreening methods to identify compounds with different thermodynamic profiles. This approach allows a reduction in the number of compounds to be tested in calorimetry experiments, thus favoring greater integration of thermodynamics in drug discovery.

  5. Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

    Science.gov (United States)

    Pankratius, V.; Gowanlock, M.; Blair, D. M.

    2015-12-01

    Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

  6. Traditional Chinese Medicine-Based Network Pharmacology Could Lead to New Multicompound Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jian Li

    2012-01-01

    Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.

  7. Constructivist Practicies Through Guided Discovery Approach: The Effect on Students' Cognitive Achievements in Nigerian Senior Secondary School Physics

    Directory of Open Access Journals (Sweden)

    A.O. Akinbobola

    2009-12-01

    Full Text Available The study investigated constructivist practices through guided discovery approach and the effect on students’ cognitive achievement in Nigerian senior secondary school Physics. The study adopted pretest-posttest control group design. A criterion sampling technique was used to select six schools out of nine schools that met the criteria. A total of 278 students took part in the study; this was made up of 141 male students and 137 female students in their respective intact classes. Physic Achievement Test (PAT with the internal consistency of 0.77 using Kuder-Richardson formula (21 was the instrument used in collecting data. The data were analyzed using Analysis of Covariance (ANCOVA and t-test. The results showed that guided discovery approaches was the most effective in facilitating students’ achievement in physics after being taught using a pictorial organizer. This was followed by demonstration while expository was found to be the least effective. Also, there exists no significant difference in the achievement of male and female physics students taught with guided discovery, demonstration and expository teaching approaches and corresponding exposure to a pictorial organizer. It is recommended that physics teachers should endeavor to use constructivist practices through guided discovery approach in order to engage students in problem solving activities, independent learning, critical thinking and understanding, and creative learning, rather than in rote learning and memorization.

  8. Fragment based drug discovery: practical implementation based on ¹⁹F NMR spectroscopy.

    Science.gov (United States)

    Jordan, John B; Poppe, Leszek; Xia, Xiaoyang; Cheng, Alan C; Sun, Yax; Michelsen, Klaus; Eastwood, Heather; Schnier, Paul D; Nixey, Thomas; Zhong, Wenge

    2012-01-26

    Fragment based drug discovery (FBDD) is a widely used tool for discovering novel therapeutics. NMR is a powerful means for implementing FBDD, and several approaches have been proposed utilizing (1)H-(15)N heteronuclear single quantum coherence (HSQC) as well as one-dimensional (1)H and (19)F NMR to screen compound mixtures against a target of interest. While proton-based NMR methods of fragment screening (FBS) have been well documented and are widely used, the use of (19)F detection in FBS has been only recently introduced (Vulpetti et al. J. Am. Chem. Soc.2009, 131 (36), 12949-12959) with the aim of targeting "fluorophilic" sites in proteins. Here, we demonstrate a more general use of (19)F NMR-based fragment screening in several areas: as a key tool for rapid and sensitive detection of fragment hits, as a method for the rapid development of structure-activity relationship (SAR) on the hit-to-lead path using in-house libraries and/or commercially available compounds, and as a quick and efficient means of assessing target druggability.

  9. Context-sensitive service discovery experimental prototype and evaluation

    DEFF Research Database (Denmark)

    Balken, Robin; Haukrogh, Jesper; L. Jensen, Jens

    2007-01-01

    The amount of different networks and services available to users today are increasing. This introduces the need for a way to locate and sort out irrelevant services in the process of discovering available services to a user. This paper describes and evaluates a prototype of an automated discovery...... and selection system, which locates services relevant to a user, based on his/her context and the context of the available services. The prototype includes a multi-level, hierarchical system approach and the introduction of entities called User-nodes, Super-nodes and Root-nodes. These entities separate...... the network in domains that handle the complex distributed service discovery, which is based on dynamically changing context information. In the prototype, a method for performing context-sensitive service discovery has been realised. The service discovery part utilizes UPnP, which has been expanded in order...

  10. From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery.

    Science.gov (United States)

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2017-01-01

    Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  11. Neural network-based QSAR and insecticide discovery: spinetoram

    Science.gov (United States)

    Sparks, Thomas C.; Crouse, Gary D.; Dripps, James E.; Anzeveno, Peter; Martynow, Jacek; DeAmicis, Carl V.; Gifford, James

    2008-06-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry.

  12. An agent-based peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises

    Science.gov (United States)

    Zhang, Wenyu; Zhang, Shuai; Cai, Ming; Jian, Wu

    2015-04-01

    With the development of virtual enterprise (VE) paradigm, the usage of serviceoriented architecture (SOA) is increasingly being considered for facilitating the integration and utilisation of distributed manufacturing resources. However, due to the heterogeneous nature among VEs, the dynamic nature of a VE and the autonomous nature of each VE member, the lack of both sophisticated coordination mechanism in the popular centralised infrastructure and semantic expressivity in the existing SOA standards make the current centralised, syntactic service discovery method undesirable. This motivates the proposed agent-based peer-to-peer (P2P) architecture for semantic discovery of manufacturing services across VEs. Multi-agent technology provides autonomous and flexible problemsolving capabilities in dynamic and adaptive VE environments. Peer-to-peer overlay provides highly scalable coupling across decentralised VEs, each of which exhibiting as a peer composed of multiple agents dealing with manufacturing services. The proposed architecture utilises a novel, efficient, two-stage search strategy - semantic peer discovery and semantic service discovery - to handle the complex searches of manufacturing services across VEs through fast peer filtering. The operation and experimental evaluation of the prototype system are presented to validate the implementation of the proposed approach.

  13. G-protein-coupled receptors: new approaches to maximise the impact of GPCRS in drug discovery.

    Science.gov (United States)

    Davey, John

    2004-04-01

    IBC's Drug Discovery Technology Series is a group of conferences highlighting technological advances and applications in niche areas of the drug discovery pipeline. This 2-day meeting focused on G-protein-coupled receptors (GPCRs), probably the most important and certainly the most valuable class of targets for drug discovery. The meeting was chaired by J Beesley (Vice President, European Business Development for LifeSpan Biosciences, Seattle, USA) and included 17 presentations on various aspects of GPCR activity, drug screens and therapeutic analyses. Keynote Addresses covered two of the emerging areas in GPCR regulation; receptor dimerisation (G Milligan, Professor of Molecular Pharmacology and Biochemistry, University of Glasgow, UK) and proteins that interact with GPCRs (J Bockaert, Laboratory of Functional Genomics, CNRS Montpellier, France). A third Keynote Address from W Thomsen (Director of GPCR Drug Screening, Arena Pharmaceuticals, USA) discussed Arena's general approach to drug discovery and illustrated this with reference to the development of an agonist with potential efficacy in Type II diabetes.

  14. Compositional descriptor-based recommender system for the materials discovery

    Science.gov (United States)

    Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao

    2018-06-01

    Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.

  15. The NCAR Research Data Archive's Hybrid Approach for Data Discovery and Access

    Science.gov (United States)

    Schuster, D.; Worley, S. J.

    2013-12-01

    The NCAR Research Data Archive (RDA http://rda.ucar.edu) maintains a variety of data discovery and access capabilities for it's 600+ dataset collections to support the varying needs of a diverse user community. In-house developed and standards-based community tools offer services to more than 10,000 users annually. By number of users the largest group is external and access the RDA through web based protocols; the internal NCAR HPC users are fewer in number, but typically access more data volume. This paper will detail the data discovery and access services maintained by the RDA to support both user groups, and show metrics that illustrate how the community is using the services. The distributed search capability enabled by standards-based community tools, such as Geoportal and an OAI-PMH access point that serves multiple metadata standards, provide pathways for external users to initially discover RDA holdings. From here, in-house developed web interfaces leverage primary discovery level metadata databases that support keyword and faceted searches. Internal NCAR HPC users, or those familiar with the RDA, may go directly to the dataset collection of interest and refine their search based on rich file collection metadata. Multiple levels of metadata have proven to be invaluable for discovery within terabyte-sized archives composed of many atmospheric or oceanic levels, hundreds of parameters, and often numerous grid and time resolutions. Once users find the data they want, their access needs may vary as well. A THREDDS data server running on targeted dataset collections enables remote file access through OPENDAP and other web based protocols primarily for external users. In-house developed tools give all users the capability to submit data subset extraction and format conversion requests through scalable, HPC based delayed mode batch processing. Users can monitor their RDA-based data processing progress and receive instructions on how to access the data when it is

  16. Topology Discovery Using Cisco Discovery Protocol

    OpenAIRE

    Rodriguez, Sergio R.

    2009-01-01

    In this paper we address the problem of discovering network topology in proprietary networks. Namely, we investigate topology discovery in Cisco-based networks. Cisco devices run Cisco Discovery Protocol (CDP) which holds information about these devices. We first compare properties of topologies that can be obtained from networks deploying CDP versus Spanning Tree Protocol (STP) and Management Information Base (MIB) Forwarding Database (FDB). Then we describe a method of discovering topology ...

  17. Effective Online Group Discovery in Trajectory Databases

    DEFF Research Database (Denmark)

    Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.

    2013-01-01

    GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independen......GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling......-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored...

  18. SemaTyP: a knowledge graph based literature mining method for drug discovery.

    Science.gov (United States)

    Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

    2018-05-30

    Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

  19. Gun possession among American youth: a discovery-based approach to understand gun violence.

    Science.gov (United States)

    Ruggles, Kelly V; Rajan, Sonali

    2014-01-01

    To apply discovery-based computational methods to nationally representative data from the Centers for Disease Control and Preventions' Youth Risk Behavior Surveillance System to better understand and visualize the behavioral factors associated with gun possession among adolescent youth. Our study uncovered the multidimensional nature of gun possession across nearly five million unique data points over a ten year period (2001-2011). Specifically, we automated odds ratio calculations for 55 risk behaviors to assemble a comprehensive table of associations for every behavior combination. Downstream analyses included the hierarchical clustering of risk behaviors based on their association "fingerprint" to 1) visualize and assess which behaviors frequently co-occur and 2) evaluate which risk behaviors are consistently found to be associated with gun possession. From these analyses, we identified more than 40 behavioral factors, including heroin use, using snuff on school property, having been injured in a fight, and having been a victim of sexual violence, that have and continue to be strongly associated with gun possession. Additionally, we identified six behavioral clusters based on association similarities: 1) physical activity and nutrition; 2) disordered eating, suicide and sexual violence; 3) weapon carrying and physical safety; 4) alcohol, marijuana and cigarette use; 5) drug use on school property and 6) overall drug use. Use of computational methodologies identified multiple risk behaviors, beyond more commonly discussed indicators of poor mental health, that are associated with gun possession among youth. Implications for prevention efforts and future interdisciplinary work applying computational methods to behavioral science data are described.

  20. Gun possession among American youth: a discovery-based approach to understand gun violence.

    Directory of Open Access Journals (Sweden)

    Kelly V Ruggles

    Full Text Available OBJECTIVE: To apply discovery-based computational methods to nationally representative data from the Centers for Disease Control and Preventions' Youth Risk Behavior Surveillance System to better understand and visualize the behavioral factors associated with gun possession among adolescent youth. RESULTS: Our study uncovered the multidimensional nature of gun possession across nearly five million unique data points over a ten year period (2001-2011. Specifically, we automated odds ratio calculations for 55 risk behaviors to assemble a comprehensive table of associations for every behavior combination. Downstream analyses included the hierarchical clustering of risk behaviors based on their association "fingerprint" to 1 visualize and assess which behaviors frequently co-occur and 2 evaluate which risk behaviors are consistently found to be associated with gun possession. From these analyses, we identified more than 40 behavioral factors, including heroin use, using snuff on school property, having been injured in a fight, and having been a victim of sexual violence, that have and continue to be strongly associated with gun possession. Additionally, we identified six behavioral clusters based on association similarities: 1 physical activity and nutrition; 2 disordered eating, suicide and sexual violence; 3 weapon carrying and physical safety; 4 alcohol, marijuana and cigarette use; 5 drug use on school property and 6 overall drug use. CONCLUSIONS: Use of computational methodologies identified multiple risk behaviors, beyond more commonly discussed indicators of poor mental health, that are associated with gun possession among youth. Implications for prevention efforts and future interdisciplinary work applying computational methods to behavioral science data are described.

  1. Next-Generation Sequencing Approaches in Genome-Wide Discovery of Single Nucleotide Polymorphism Markers Associated with Pungency and Disease Resistance in Pepper.

    Science.gov (United States)

    Manivannan, Abinaya; Kim, Jin-Hee; Yang, Eun-Young; Ahn, Yul-Kyun; Lee, Eun-Su; Choi, Sena; Kim, Do-Sun

    2018-01-01

    Pepper is an economically important horticultural plant that has been widely used for its pungency and spicy taste in worldwide cuisines. Therefore, the domestication of pepper has been carried out since antiquity. Owing to meet the growing demand for pepper with high quality, organoleptic property, nutraceutical contents, and disease tolerance, genomics assisted breeding techniques can be incorporated to develop novel pepper varieties with desired traits. The application of next-generation sequencing (NGS) approaches has reformed the plant breeding technology especially in the area of molecular marker assisted breeding. The availability of genomic information aids in the deeper understanding of several molecular mechanisms behind the vital physiological processes. In addition, the NGS methods facilitate the genome-wide discovery of DNA based markers linked to key genes involved in important biological phenomenon. Among the molecular markers, single nucleotide polymorphism (SNP) indulges various benefits in comparison with other existing DNA based markers. The present review concentrates on the impact of NGS approaches in the discovery of useful SNP markers associated with pungency and disease resistance in pepper. The information provided in the current endeavor can be utilized for the betterment of pepper breeding in future.

  2. Next-Generation Sequencing Approaches in Genome-Wide Discovery of Single Nucleotide Polymorphism Markers Associated with Pungency and Disease Resistance in Pepper

    Directory of Open Access Journals (Sweden)

    Abinaya Manivannan

    2018-01-01

    Full Text Available Pepper is an economically important horticultural plant that has been widely used for its pungency and spicy taste in worldwide cuisines. Therefore, the domestication of pepper has been carried out since antiquity. Owing to meet the growing demand for pepper with high quality, organoleptic property, nutraceutical contents, and disease tolerance, genomics assisted breeding techniques can be incorporated to develop novel pepper varieties with desired traits. The application of next-generation sequencing (NGS approaches has reformed the plant breeding technology especially in the area of molecular marker assisted breeding. The availability of genomic information aids in the deeper understanding of several molecular mechanisms behind the vital physiological processes. In addition, the NGS methods facilitate the genome-wide discovery of DNA based markers linked to key genes involved in important biological phenomenon. Among the molecular markers, single nucleotide polymorphism (SNP indulges various benefits in comparison with other existing DNA based markers. The present review concentrates on the impact of NGS approaches in the discovery of useful SNP markers associated with pungency and disease resistance in pepper. The information provided in the current endeavor can be utilized for the betterment of pepper breeding in future.

  3. A P2P Service Discovery Strategy Based on Content Catalogues

    Directory of Open Access Journals (Sweden)

    Lican Huang

    2007-08-01

    Full Text Available This paper presents a framework for distributed service discovery based on VIRGO P2P technologies. The services are classified as multi-layer, hierarchical catalogue domains according to their contents. The service providers, which have their own service registries such as UDDIs, register the services they provide and establish a virtual tree in a VIRGO network according to the domain of their service. The service location done by the proposed strategy is effective and guaranteed. This paper also discusses the primary implementation of service discovery based on Tomcat/Axis and jUDDI.

  4. Comment on "drug discovery: turning the titanic".

    Science.gov (United States)

    Lesterhuis, W Joost; Bosco, Anthony; Lake, Richard A

    2014-03-26

    The pathobiology-based approach to research and development has been the dominant paradigm for successful drug discovery over the last decades. We propose that the molecular and cellular events that govern a resolving, rather than an evolving, disease may reveal new druggable pathways.

  5. HANDS: a tool for genome-wide discovery of subgenome-specific base-identity in polyploids.

    KAUST Repository

    Mithani, Aziz; Belfield, Eric J; Brown, Carly; Jiang, Caifu; Leach, Lindsey J; Harberd, Nicholas P

    2013-01-01

    The analysis of polyploid genomes is problematic because homeologous subgenome sequences are closely related. This relatedness makes it difficult to assign individual sequences to the specific subgenome from which they are derived, and hinders the development of polyploid whole genome assemblies.We here present a next-generation sequencing (NGS)-based approach for assignment of subgenome-specific base-identity at sites containing homeolog-specific polymorphisms (HSPs): 'HSP base Assignment using NGS data through Diploid Similarity' (HANDS). We show that HANDS correctly predicts subgenome-specific base-identity at >90% of assayed HSPs in the hexaploid bread wheat (Triticum aestivum) transcriptome, thus providing a substantial increase in accuracy versus previous methods for homeolog-specific base assignment.We conclude that HANDS enables rapid and accurate genome-wide discovery of homeolog-specific base-identity, a capability having multiple applications in polyploid genomics.

  6. HANDS: a tool for genome-wide discovery of subgenome-specific base-identity in polyploids.

    KAUST Repository

    Mithani, Aziz

    2013-09-24

    The analysis of polyploid genomes is problematic because homeologous subgenome sequences are closely related. This relatedness makes it difficult to assign individual sequences to the specific subgenome from which they are derived, and hinders the development of polyploid whole genome assemblies.We here present a next-generation sequencing (NGS)-based approach for assignment of subgenome-specific base-identity at sites containing homeolog-specific polymorphisms (HSPs): \\'HSP base Assignment using NGS data through Diploid Similarity\\' (HANDS). We show that HANDS correctly predicts subgenome-specific base-identity at >90% of assayed HSPs in the hexaploid bread wheat (Triticum aestivum) transcriptome, thus providing a substantial increase in accuracy versus previous methods for homeolog-specific base assignment.We conclude that HANDS enables rapid and accurate genome-wide discovery of homeolog-specific base-identity, a capability having multiple applications in polyploid genomics.

  7. Synthetic biology approaches in drug discovery and pharmaceutical biotechnology.

    Science.gov (United States)

    Neumann, Heinz; Neumann-Staubitz, Petra

    2010-06-01

    Synthetic biology is the attempt to apply the concepts of engineering to biological systems with the aim to create organisms with new emergent properties. These organisms might have desirable novel biosynthetic capabilities, act as biosensors or help us to understand the intricacies of living systems. This approach has the potential to assist the discovery and production of pharmaceutical compounds at various stages. New sources of bioactive compounds can be created in the form of genetically encoded small molecule libraries. The recombination of individual parts has been employed to design proteins that act as biosensors, which could be used to identify and quantify molecules of interest. New biosynthetic pathways may be designed by stitching together enzymes with desired activities, and genetic code expansion can be used to introduce new functionalities into peptides and proteins to increase their chemical scope and biological stability. This review aims to give an insight into recently developed individual components and modules that might serve as parts in a synthetic biology approach to pharmaceutical biotechnology.

  8. Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.

    Science.gov (United States)

    Potemkin, Andrey V; Grishina, Maria A; Potemkin, Vladimir A

    2017-01-01

    In 1979, R.D.Cramer and M.Milne made a first realization of 3D comparison of molecules by aligning them in space and by mapping their molecular fields to a 3D grid. Further, this approach was developed as the DYLOMMS (Dynamic Lattice- Oriented Molecular Modelling System) approach. In 1984, H.Wold and S.Wold proposed the use of partial least squares (PLS) analysis, instead of principal component analysis, to correlate the field values with biological activities. Then, in 1988, the method which was called CoMFA (Comparative Molecular Field Analysis) was introduced and the appropriate software became commercially available. Since 1988, a lot of 3D QSAR methods, algorithms and their modifications are introduced for solving of virtual drug discovery problems (e.g., CoMSIA, CoMMA, HINT, HASL, GOLPE, GRID, PARM, Raptor, BiS, CiS, ConGO,). All the methods can be divided into two groups (classes):1. Methods studying the exterior of molecules; 2) Methods studying the interior of molecules. A series of grid-based computational technologies for Continual Molecular Interior analysis (CoMIn) are invented in the current paper. The grid-based analysis is fulfilled by means of a lattice construction analogously to many other grid-based methods. The further continual elucidation of molecular structure is performed in various ways. (i) In terms of intermolecular interactions potentials. This can be represented as a superposition of Coulomb, Van der Waals interactions and hydrogen bonds. All the potentials are well known continual functions and their values can be determined in all lattice points for a molecule. (ii) In the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum

  9. Fragment-based drug discovery and molecular docking in drug design.

    Science.gov (United States)

    Wang, Tao; Wu, Mian-Bin; Chen, Zheng-Jie; Chen, Hua; Lin, Jian-Ping; Yang, Li-Rong

    2015-01-01

    Fragment-based drug discovery (FBDD) has caused a revolution in the process of drug discovery and design, with many FBDD leads being developed into clinical trials or approved in the past few years. Compared with traditional high-throughput screening, it displays obvious advantages such as efficiently covering chemical space, achieving higher hit rates, and so forth. In this review, we focus on the most recent developments of FBDD for improving drug discovery, illustrating the process and the importance of FBDD. In particular, the computational strategies applied in the process of FBDD and molecular-docking programs are highlighted elaborately. In most cases, docking is used for predicting the ligand-receptor interaction modes and hit identification by structurebased virtual screening. The successful cases of typical significance and the hits identified most recently are discussed.

  10. Mass Spectrometry–Based Biomarker Discovery: Toward a Global Proteome Index of Individuality

    Science.gov (United States)

    Hawkridge, Adam M.; Muddiman, David C.

    2011-01-01

    Biomarker discovery and proteomics have become synonymous with mass spectrometry in recent years. Although this conflation is an injustice to the many essential biomolecular techniques widely used in biomarker-discovery platforms, it underscores the power and potential of contemporary mass spectrometry. Numerous novel and powerful technologies have been developed around mass spectrometry, proteomics, and biomarker discovery over the past 20 years to globally study complex proteomes (e.g., plasma). However, very few large-scale longitudinal studies have been carried out using these platforms to establish the analytical variability relative to true biological variability. The purpose of this review is not to cover exhaustively the applications of mass spectrometry to biomarker discovery, but rather to discuss the analytical methods and strategies that have been developed for mass spectrometry–based biomarker-discovery platforms and to place them in the context of the many challenges and opportunities yet to be addressed. PMID:20636062

  11. promoting self directed learning in simulation based discovery learning environments through intelligent support.

    NARCIS (Netherlands)

    Veermans, K.H.; de Jong, Anthonius J.M.; van Joolingen, Wouter

    2000-01-01

    Providing learners with computer-generated feedback on their learning process in simulationbased discovery environments cannot be based on a detailed model of the learning process due to the “open” character of discovery learning. This paper describes a method for generating adaptive feedback for

  12. A feature-based approach for best arm identification in the case of the Monte Carlo search algorithm discovery for one-player games

    OpenAIRE

    Taralla, David

    2013-01-01

    The field of reinforcement learning recently received the contribution by Ernst et al. (2013) "Monte carlo search algorithm discovery for one player games" who introduced a new way to conceive completely new algorithms. Moreover, it brought an automatic method to find the best algorithm to use in a particular situation using a multi-arm bandit approach. We address here the problem of best arm identification. The main problem is that the generated algorithm space (ie. the arm space) can be qui...

  13. Student Responses Toward Student Worksheets Based on Discovery Learning for Students with Intrapersonal and Interpersonal Intelligence

    Science.gov (United States)

    Yerizon, Y.; Putra, A. A.; Subhan, M.

    2018-04-01

    Students have a low mathematical ability because they are used to learning to hear the teacher's explanation. For that students are given activities to sharpen his ability in math. One way to do that is to create discovery learning based work sheet. The development of this worksheet took into account specific student learning styles including in schools that have classified students based on multiple intelligences. The dominant learning styles in the classroom were intrapersonal and interpersonal. The purpose of this study was to discover students’ responses to the mathematics work sheets of the junior high school with a discovery learning approach suitable for students with Intrapersonal and Interpersonal Intelligence. This tool was developed using a development model adapted from the Plomp model. The development process of this tools consists of 3 phases: front-end analysis/preliminary research, development/prototype phase and assessment phase. From the results of the research, it is found that students have good response to the resulting work sheet. The worksheet was understood well by students and its helps student in understanding the concept learned.

  14. Straightforward hit identification approach in fragment-based discovery of bromodomain-containing protein 4 (BRD4) inhibitors.

    Science.gov (United States)

    Borysko, Petro; Moroz, Yurii S; Vasylchenko, Oleksandr V; Hurmach, Vasyl V; Starodubtseva, Anastasia; Stefanishena, Natalia; Nesteruk, Kateryna; Zozulya, Sergey; Kondratov, Ivan S; Grygorenko, Oleksandr O

    2018-05-09

    A combination approach of a fragment screening and "SAR by catalog" was used for the discovery of bromodomain-containing protein 4 (BRD4) inhibitors. Initial screening of 3695-fragment library against bromodomain 1 of BRD4 using thermal shift assay (TSA), followed by initial hit validation, resulted in 73 fragment hits, which were used to construct a follow-up library selected from available screening collection. Additionally, analogs of inactive fragments, as well as a set of randomly selected compounds were also prepared (3 × 3200 compounds in total). Screening of the resulting sets using TSA, followed by re-testing at several concentrations, counter-screen, and TR-FRET assay resulted in 18 confirmed hits. Compounds derived from the initial fragment set showed better hit rate as compared to the other two sets. Finally, building dose-response curves revealed three compounds with IC 50  = 1.9-7.4 μM. For these compounds, binding sites and conformations in the BRD4 (4UYD) have been determined by docking. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Microwave-Assisted Esterification: A Discovery-Based Microscale Laboratory Experiment

    Science.gov (United States)

    Reilly, Maureen K.; King, Ryan P.; Wagner, Alexander J.; King, Susan M.

    2014-01-01

    An undergraduate organic chemistry laboratory experiment has been developed that features a discovery-based microscale Fischer esterification utilizing a microwave reactor. Students individually synthesize a unique ester from known sets of alcohols and carboxylic acids. Each student identifies the best reaction conditions given their particular…

  16. Maximum Entropy in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Chih-Yuan Tseng

    2014-07-01

    Full Text Available Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes’ pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.

  17. The Universe Discovery Guides: A Collaborative Approach to Educating with NASA Science

    Science.gov (United States)

    Manning, James G.; Lawton, Brandon L.; Gurton, Suzanne; Smith, Denise Anne; Schultz, Gregory; Astrophysics Community, NASA

    2015-08-01

    For the 2009 International Year of Astronomy, the then-existing NASA Origins Forum collaborated with the Astronomical Society of the Pacific (ASP) to create a series of monthly “Discovery Guides” for informal educator and amateur astronomer use in educating the public about featured sky objects and associated NASA science themes. Today’s NASA Astrophysics Science Education and Public Outreach Forum (SEPOF), one of the current generation of forums coordinating the work of NASA Science Mission Directorate (SMD) EPO efforts—in collaboration with the ASP and NASA SMD missions and programs--has adapted the Discovery Guides into “evergreen” educational resources suitable for a variety of audiences. The Guides focus on “deep sky” objects and astrophysics themes (stars and stellar evolution, galaxies and the universe, and exoplanets), showcasing EPO resources from more than 30 NASA astrophysics missions and programs in a coordinated and cohesive “big picture” approach across the electromagnetic spectrum, grounded in best practices to best serve the needs of the target audiences.Each monthly guide features a theme and a representative object well-placed for viewing, with an accompanying interpretive story, finding charts, strategies for conveying the topics, and complementary supporting NASA-approved education activities and background information from a spectrum of NASA missions and programs. The Universe Discovery Guides are downloadable from the NASA Night Sky Network web site at nightsky.jpl.nasa.gov and specifically from http://nightsky.jpl.nasa.gov/news-display.cfm?News_ID=611.The presentation will describe the collaborative’s experience in developing the guides, how they place individual science discoveries and learning resources into context for audiences, and how the Guides can be readily used in scientist public outreach efforts, in college and university introductory astronomy classes, and in other engagements between scientists, instructors

  18. Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

    Science.gov (United States)

    Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

    2011-08-01

    The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. The heat is on: thermodynamic analysis in fragment-based drug discovery

    NARCIS (Netherlands)

    Edink, E.S.; Jansen, C.J.W.; Leurs, R.; De Esch, I.J.

    2010-01-01

    Thermodynamic analysis provides access to the determinants of binding affinity, enthalpy and entropy. In fragment-based drug discovery (FBDD), thermodynamic analysis provides a powerful tool to discriminate fragments based on their potential for successful optimization. The thermodynamic data

  20. A Novel Mobile Video Community Discovery Scheme Using Ontology-Based Semantical Interest Capture

    Directory of Open Access Journals (Sweden)

    Ruiling Zhang

    2016-01-01

    Full Text Available Leveraging network virtualization technologies, the community-based video systems rely on the measurement of common interests to define and steady relationship between community members, which promotes video sharing performance and improves scalability community structure. In this paper, we propose a novel mobile Video Community discovery scheme using ontology-based semantical interest capture (VCOSI. An ontology-based semantical extension approach is proposed, which describes video content and measures video similarity according to video key word selection methods. In order to reduce the calculation load of video similarity, VCOSI designs a prefix-filtering-based estimation algorithm to decrease energy consumption of mobile nodes. VCOSI further proposes a member relationship estimate method to construct scalable and resilient node communities, which promotes video sharing capacity of video systems with the flexible and economic community maintenance. Extensive tests show how VCOSI obtains better performance results in comparison with other state-of-the-art solutions.

  1. An Ontology-supported Approach for Automatic Chaining of Web Services in Geospatial Knowledge Discovery

    Science.gov (United States)

    di, L.; Yue, P.; Yang, W.; Yu, G.

    2006-12-01

    Recent developments in geospatial semantic Web have shown promise for automatic discovery, access, and use of geospatial Web services to quickly and efficiently solve particular application problems. With the semantic Web technology, it is highly feasible to construct intelligent geospatial knowledge systems that can provide answers to many geospatial application questions. A key challenge in constructing such intelligent knowledge system is to automate the creation of a chain or process workflow that involves multiple services and highly diversified data and can generate the answer to a specific question of users. This presentation discusses an approach for automating composition of geospatial Web service chains by employing geospatial semantics described by geospatial ontologies. It shows how ontology-based geospatial semantics are used for enabling the automatic discovery, mediation, and chaining of geospatial Web services. OWL-S is used to represent the geospatial semantics of individual Web services and the type of the services it belongs to and the type of the data it can handle. The hierarchy and classification of service types are described in the service ontology. The hierarchy and classification of data types are presented in the data ontology. For answering users' geospatial questions, an Artificial Intelligent (AI) planning algorithm is used to construct the service chain by using the service and data logics expressed in the ontologies. The chain can be expressed as a graph with nodes representing services and connection weights representing degrees of semantic matching between nodes. The graph is a visual representation of logical geo-processing path for answering users' questions. The graph can be instantiated to a physical service workflow for execution to generate the answer to a user's question. A prototype system, which includes real world geospatial applications, is implemented to demonstrate the concept and approach.

  2. Context discovery using attenuated Bloom codes: model description and validation

    NARCIS (Netherlands)

    Liu, F.; Heijenk, Geert

    A novel approach to performing context discovery in ad-hoc networks based on the use of attenuated Bloom filters is proposed in this report. In order to investigate the performance of this approach, a model has been developed. This document describes the model and its validation. The model has been

  3. Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

    Science.gov (United States)

    Hulshof, Casper D.; de Jong, Ton

    2006-01-01

    Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

  4. National Heart, Lung, and Blood Institute and the translation of cardiovascular discoveries into therapeutic approaches.

    Science.gov (United States)

    Galis, Zorina S; Black, Jodi B; Skarlatos, Sonia I

    2013-04-26

    The molecular causes of ≈4000 medical conditions have been described, yet only 5% have associated therapies. For decades, the average time for drug development through approval has taken 10 to 20 years. In recent years, the serious challenges that confront the private sector have made it difficult to capitalize on new opportunities presented by advances in genomics and cellular therapies. Current trends are disturbing. Pharmaceutical companies are reducing their investments in research, and biotechnology companies are struggling to obtain venture funds. To support early-stage translation of the discoveries in basic science, the National Institutes of Health and the National Heart, Lung, and Blood Institute have developed new approaches to facilitating the translation of basic discoveries into clinical applications and will continue to develop a variety of programs that create teams of academic investigators and industry partners. The goal of these programs is to maximize the public benefit of investment of taxpayer dollars in biomedical research and to lessen the risk required for industry partners to make substantial investments. This article highlights several examples of National Heart, Lung, and Blood Institute-initiated translational programs and National Institutes of Health translational resources designed to catalyze and enable the earliest stages of the biomedical product development process. The translation of latest discoveries into therapeutic approaches depends on continued federal funding to enhance the early stages of the product development process and to stimulate and catalyze partnerships between academia, industry, and other sources of capital.

  5. Mars extant-life campaign using an approach based on Earth-analog habitats

    Science.gov (United States)

    Palkovic, Lawrence A.; Wilson, Thomas J.

    2005-01-01

    The Mars Robotic Outpost group at JPL has identified sixteen potential momentous discoveries that if found on Mars would alter planning for the future Mars exploration program. This paper details one possible approach to the discovery of and response to the 'momentous discovery'' of extant life on Mars. The approach detailed in this paper, the Mars Extant-Life (MEL) campaign, is a comprehensive and flexible program to find living organisms on Mars by studying Earth-analog habitats of extremophile communities.

  6. Knowledge discovery about quality of life changes of spinal cord injury patients: clustering based on rules by states.

    Science.gov (United States)

    Gibert, Karina; García-Rudolph, Alejandro; Curcoll, Lluïsa; Soler, Dolors; Pla, Laura; Tormos, José María

    2009-01-01

    In this paper, an integral Knowledge Discovery Methodology, named Clustering based on rules by States, which incorporates artificial intelligence (AI) and statistical methods as well as interpretation-oriented tools, is used for extracting knowledge patterns about the evolution over time of the Quality of Life (QoL) of patients with Spinal Cord Injury. The methodology incorporates the interaction with experts as a crucial element with the clustering methodology to guarantee usefulness of the results. Four typical patterns are discovered by taking into account prior expert knowledge. Several hypotheses are elaborated about the reasons for psychological distress or decreases in QoL of patients over time. The knowledge discovery from data (KDD) approach turns out, once again, to be a suitable formal framework for handling multidimensional complexity of the health domains.

  7. Equation Discovery for Financial Forcasting in Context of Islamic Banking

    Institute of Scientific and Technical Information of China (English)

    Amer Alzaidi; Dimitar Kazakov

    2010-01-01

    This paper describes an equation discovery approach based on machine leamng using LAGdtAMGE as an equation discovery tool,with two sources of input,a dataset and model presented in context-free gammar.The approach is searching a large range of potential equations by a specific model.The parameters of the equation are fitted to find the best equations.The The experiments are illustratedwith commodity prices from the London Metal Exchange for the period of January-October 2009.The outputs of the experiments are a large number of equations;same of the equations display that the predicted rakes are following the market trends in perfect patterns.

  8. Augmented Reality-Based Simulators as Discovery Learning Tools: An Empirical Study

    Science.gov (United States)

    Ibáñez, María-Blanca; Di-Serio, Ángela; Villarán-Molina, Diego; Delgado-Kloos, Carlos

    2015-01-01

    This paper reports empirical evidence on having students use AR-SaBEr, a simulation tool based on augmented reality (AR), to discover the basic principles of electricity through a series of experiments. AR-SaBEr was enhanced with knowledge-based support and inquiry-based scaffolding mechanisms, which proved useful for discovery learning in…

  9. Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches.

    Science.gov (United States)

    Oliynyk, Anton O; Mar, Arthur

    2018-01-16

    Intermetallic compounds are bestowed by diverse compositions, complex structures, and useful properties for many materials applications. How metallic elements react to form these compounds and what structures they adopt remain challenging questions that defy predictability. Traditional approaches offer some rational strategies to prepare specific classes of intermetallics, such as targeting members within a modular homologous series, manipulating building blocks to assemble new structures, and filling interstitial sites to create stuffed variants. Because these strategies rely on precedent, they cannot foresee surprising results, by definition. Exploratory synthesis, whether through systematic phase diagram investigations or serendipity, is still essential for expanding our knowledge base. Eventually, the relationships may become too complex for the pattern recognition skills to be reliably or practically performed by humans. Complementing these traditional approaches, new machine-learning approaches may be a viable alternative for materials discovery, not only among intermetallics but also more generally to other chemical compounds. In this Account, we survey our own efforts to discover new intermetallic compounds, encompassing gallides, germanides, phosphides, arsenides, and others. We apply various machine-learning methods (such as support vector machine and random forest algorithms) to confront two significant questions in solid state chemistry. First, what crystal structures are adopted by a compound given an arbitrary composition? Initial efforts have focused on binary equiatomic phases AB, ternary equiatomic phases ABC, and full Heusler phases AB 2 C. Our analysis emphasizes the use of real experimental data and places special value on confirming predictions through experiment. Chemical descriptors are carefully chosen through a rigorous procedure called cluster resolution feature selection. Predictions for crystal structures are quantified by evaluating

  10. Discovery of accessible locations using region-based geo-social data

    KAUST Repository

    Wang, Yan; Li, Jianmin; Zhong, Ying; Zhu, Shunzhi; Guo, Danhuai; Shang, Shuo

    2018-01-01

    Geo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query

  11. Aptamer-based multiplexed proteomic technology for biomarker discovery.

    Directory of Open Access Journals (Sweden)

    Larry Gold

    2010-12-01

    Full Text Available The interrogation of proteomes ("proteomics" in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma. Our current assay measures 813 proteins with low limits of detection (1 pM median, 7 logs of overall dynamic range (~100 fM-1 µM, and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD. We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

  12. Aptamer-based multiplexed proteomic technology for biomarker discovery.

    Science.gov (United States)

    Gold, Larry; Ayers, Deborah; Bertino, Jennifer; Bock, Christopher; Bock, Ashley; Brody, Edward N; Carter, Jeff; Dalby, Andrew B; Eaton, Bruce E; Fitzwater, Tim; Flather, Dylan; Forbes, Ashley; Foreman, Trudi; Fowler, Cate; Gawande, Bharat; Goss, Meredith; Gunn, Magda; Gupta, Shashi; Halladay, Dennis; Heil, Jim; Heilig, Joe; Hicke, Brian; Husar, Gregory; Janjic, Nebojsa; Jarvis, Thale; Jennings, Susan; Katilius, Evaldas; Keeney, Tracy R; Kim, Nancy; Koch, Tad H; Kraemer, Stephan; Kroiss, Luke; Le, Ngan; Levine, Daniel; Lindsey, Wes; Lollo, Bridget; Mayfield, Wes; Mehan, Mike; Mehler, Robert; Nelson, Sally K; Nelson, Michele; Nieuwlandt, Dan; Nikrad, Malti; Ochsner, Urs; Ostroff, Rachel M; Otis, Matt; Parker, Thomas; Pietrasiewicz, Steve; Resnicow, Daniel I; Rohloff, John; Sanders, Glenn; Sattin, Sarah; Schneider, Daniel; Singer, Britta; Stanton, Martin; Sterkel, Alana; Stewart, Alex; Stratford, Suzanne; Vaught, Jonathan D; Vrkljan, Mike; Walker, Jeffrey J; Watrobka, Mike; Waugh, Sheela; Weiss, Allison; Wilcox, Sheri K; Wolfson, Alexey; Wolk, Steven K; Zhang, Chi; Zichi, Dom

    2010-12-07

    The interrogation of proteomes ("proteomics") in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (~100 fM-1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states. We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

  13. Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma.

    Science.gov (United States)

    Fujii, Kiyonaga; Nakamura, Haruhiko; Nishimura, Toshihide

    2017-04-01

    Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.

  14. A Discovery-Based Hydrochlorination of Carvone Utilizing a Guided-Inquiry Approach to Determine the Product Structure from [superscript 13]C NMR Spectra

    Science.gov (United States)

    Pelter, Michael W.; Walker, Natalie M.

    2012-01-01

    This experiment describes a discovery-based method for the regio- and stereoselective hydrochlorination of carvone, appropriate for a 3-h second-semester organic chemistry laboratory. The product is identified through interpretation of the [superscript 13]C NMR and DEPT spectra are obtained on an Anasazi EFT-60 at 15 MHz as neat samples. A…

  15. From Medicinal Chemistry to Human Health: Current Approaches to Drug Discovery for Cancer and Neglected Tropical Diseases

    Directory of Open Access Journals (Sweden)

    LEONARDO G. FERREIRA

    2018-02-01

    Full Text Available ABSTRACT Scientific and technological breakthroughs have compelled the current players in drug discovery to increasingly incorporate knowledge-based approaches. This evolving paradigm, which has its roots attached to the recent advances in medicinal chemistry, molecular and structural biology, has unprecedentedly demanded the development of up-to-date computational approaches, such as bio- and chemo-informatics. These tools have been pivotal to catalyzing the ever-increasing amount of data generated by the molecular sciences, and to converting the data into insightful guidelines for use in the research pipeline. As a result, ligand- and structure-based drug design have emerged as key pathways to address the pharmaceutical industry’s striking demands for innovation. These approaches depend on a keen integration of experimental and molecular modeling methods to surmount the main challenges faced by drug candidates - in vivo efficacy, pharmacodynamics, metabolism, pharmacokinetics and safety. To that end, the Laboratório de Química Medicinal e Computacional (LQMC of the Universidade de São Paulo has developed forefront research on highly prevalent and life-threatening neglected tropical diseases and cancer. By taking part in global initiatives for pharmaceutical innovation, the laboratory has contributed to the advance of these critical therapeutic areas through the use of cutting-edge strategies in medicinal chemistry.

  16. A DHT-Based Discovery Service for the Internet of Things

    Directory of Open Access Journals (Sweden)

    Federica Paganelli

    2012-01-01

    Full Text Available Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users’ quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of “smart things” on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier, our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation.

  17. Evolutions in fragment-based drug design: the deconstruction–reconstruction approach

    Science.gov (United States)

    Chen, Haijun; Zhou, Xiaobin; Wang, Ailan; Zheng, Yunquan; Gao, Yu; Zhou, Jia

    2014-01-01

    Recent advances in the understanding of molecular recognition and protein–ligand interactions have facilitated rapid development of potent and selective ligands for therapeutically relevant targets. Over the past two decades, a variety of useful approaches and emerging techniques have been developed to promote the identification and optimization of leads that have high potential for generating new therapeutic agents. Intriguingly, the innovation of a fragment-based drug design (FBDD) approach has enabled rapid and efficient progress in drug discovery. In this critical review, we focus on the construction of fragment libraries and the advantages and disadvantages of various fragment-based screening (FBS) for constructing such libraries. We also highlight the deconstruction–reconstruction strategy by utilizing privileged fragments of reported ligands. PMID:25263697

  18. The SGC beyond structural genomics: redefining the role of 3D structures by coupling genomic stratification with fragment-based discovery.

    Science.gov (United States)

    Bradley, Anthony R; Echalier, Aude; Fairhead, Michael; Strain-Damerell, Claire; Brennan, Paul; Bullock, Alex N; Burgess-Brown, Nicola A; Carpenter, Elisabeth P; Gileadi, Opher; Marsden, Brian D; Lee, Wen Hwa; Yue, Wyatt; Bountra, Chas; von Delft, Frank

    2017-11-08

    The ongoing explosion in genomics data has long since outpaced the capacity of conventional biochemical methodology to verify the large number of hypotheses that emerge from the analysis of such data. In contrast, it is still a gold-standard for early phenotypic validation towards small-molecule drug discovery to use probe molecules (or tool compounds), notwithstanding the difficulty and cost of generating them. Rational structure-based approaches to ligand discovery have long promised the efficiencies needed to close this divergence; in practice, however, this promise remains largely unfulfilled, for a host of well-rehearsed reasons and despite the huge technical advances spearheaded by the structural genomics initiatives of the noughties. Therefore the current, fourth funding phase of the Structural Genomics Consortium (SGC), building on its extensive experience in structural biology of novel targets and design of protein inhibitors, seeks to redefine what it means to do structural biology for drug discovery. We developed the concept of a Target Enabling Package (TEP) that provides, through reagents, assays and data, the missing link between genetic disease linkage and the development of usefully potent compounds. There are multiple prongs to the ambition: rigorously assessing targets' genetic disease linkages through crowdsourcing to a network of collaborating experts; establishing a systematic approach to generate the protocols and data that comprise each target's TEP; developing new, X-ray-based fragment technologies for generating high quality chemical matter quickly and cheaply; and exploiting a stringently open access model to build multidisciplinary partnerships throughout academia and industry. By learning how to scale these approaches, the SGC aims to make structures finally serve genomics, as originally intended, and demonstrate how 3D structures systematically allow new modes of druggability to be discovered for whole classes of targets. © 2017 The

  19. The Implementation of Discovery Learning Model with Scientific Learning Approach to Improve Students’ Critical Thinking in Learning History

    Directory of Open Access Journals (Sweden)

    Edi Nurcahyo

    2018-03-01

    Full Text Available Historical learning has not reached optimal in the learning process. It is caused by the history teachers’ learning model has not used the innovative learning models. Furthermore, it supported by the perception of students to the history subject because it does not become final exam (UN subject so it makes less improvement and builds less critical thinking in students’ daily learning. This is due to the lack of awareness of historical events and the availability of history books for students and teachers in the library are still lacking. Discovery learning with scientific approach encourages students to solve problems actively and able to improve students' critical thinking skills with scientific approach so student can build scientific thinking include observing, asking, reasoning, trying, and networking   Keywords: discovery learning, scientific, critical thinking

  20. Node Discovery and Interpretation in Unstructured Resource-Constrained Environments

    DEFF Research Database (Denmark)

    Gechev, Miroslav; Kasabova, Slavyana; Mihovska, Albena D.

    2014-01-01

    for the discovery, linking and interpretation of nodes in unstructured and resource-constrained network environments and their interrelated and collective use for the delivery of smart services. The model is based on a basic mathematical approach, which describes and predicts the success of human interactions...... in the context of long-term relationships and identifies several key variables in the context of communications in resource-constrained environments. The general theoretical model is described and several algorithms are proposed as part of the node discovery, identification, and linking processes in relation...

  1. A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

    Directory of Open Access Journals (Sweden)

    Ahu Sokmenoglu Sohtorik

    2017-11-01

    Full Text Available Enhancing our knowledge of the complexities of cities in order to empower ourselves to make more informed decisions has always been a challenge for urban research. Recent developments in large-scale computing, together with the new techniques and automated tools for data collection and analysis are opening up promising opportunities for addressing this problem. The main motivation that served as the driving force behind this research is how these developments may contribute to urban data analysis. On this basis, the thesis focuses on urban data analysis in order to search for findings that can enhance our knowledge of urban environments, using the generic process of knowledge discovery using data mining. A knowledge discovery process based on data mining is a fully automated or semi-automated process which involves the application of computational tools and techniques to explore the “previously unknown, and potentially useful information” (Witten & Frank, 2005 hidden in large and often complex and multi-dimensional databases. This information can be obtained in the form of correlations amongst variables, data groupings (classes and clusters or more complex hypotheses (probabilistic rules of co-occurrence, performance vectors of prediction models etc.. This research targets researchers and practitioners working in the field of urban studies who are interested in quantitative/ computational approaches to urban data analysis and specifically aims to engage the interest of architects, urban designers and planners who do not have a background in statistics or in using data mining methods in their work. Accordingly, the overall aim of the thesis is the development of a knowledge discovery approach to urban analysis; a domain-specific adaptation of the generic process of knowledge discovery using data mining enabling the analyst to discover ‘relational urban knowledge’. ‘Relational urban knowledge’ is a term employed in this thesis to refer

  2. Bioinformatics and biomarker discovery "Omic" data analysis for personalized medicine

    CERN Document Server

    Azuaje, Francisco

    2010-01-01

    This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided w

  3. ACFIS: a web server for fragment-based drug discovery

    Science.gov (United States)

    Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

    2016-01-01

    In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. PMID:27150808

  4. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

    Science.gov (United States)

    Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

    2016-01-01

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

  5. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach.

    Science.gov (United States)

    Marco-Ruiz, Luis; Pedrinaci, Carlos; Maldonado, J A; Panziera, Luca; Chen, Rong; Bellika, J Gustav

    2016-08-01

    The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services' interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies. To facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data. We developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data. We developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built. Linked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine

  6. RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

    Science.gov (United States)

    Tilton, James C.; Cook, Diane J.

    2008-01-01

    Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.

  7. The Effect of Concept Mapping-Guided Discovery Integrated Teaching Approach on Chemistry Students' Achievement and Retention

    Science.gov (United States)

    Fatokun, K. V. F.; Eniayeju, P. A.

    2014-01-01

    This study investigates the effects of Concept Mapping-Guided Discovery Integrated Teaching Approach on the achievement and retention of chemistry students. The sample comprised 162 Senior Secondary two (SS 2) students drawn from two Science Schools in Nasarawa State, Central Nigeria with equivalent mean scores of 9.68 and 9.49 in their pre-test.…

  8. Lessons from hot spot analysis for fragment-based drug discovery

    Science.gov (United States)

    Hall, David R.; Vajda, Sandor

    2015-01-01

    Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high affinity, druglike ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from protein three-dimensional structure, and how their strength, number and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. PMID:26538314

  9. Synthetic biology of antimicrobial discovery.

    Science.gov (United States)

    Zakeri, Bijan; Lu, Timothy K

    2013-07-19

    Antibiotic discovery has a storied history. From the discovery of penicillin by Sir Alexander Fleming to the relentless quest for antibiotics by Selman Waksman, the stories have become like folklore used to inspire future generations of scientists. However, recent discovery pipelines have run dry at a time when multidrug-resistant pathogens are on the rise. Nature has proven to be a valuable reservoir of antimicrobial agents, which are primarily produced by modularized biochemical pathways. Such modularization is well suited to remodeling by an interdisciplinary approach that spans science and engineering. Herein, we discuss the biological engineering of small molecules, peptides, and non-traditional antimicrobials and provide an overview of the growing applicability of synthetic biology to antimicrobials discovery.

  10. Towards a semantics-based approach in the development of geographic portals

    Science.gov (United States)

    Athanasis, Nikolaos; Kalabokidis, Kostas; Vaitis, Michail; Soulakellis, Nikolaos

    2009-02-01

    As the demand for geospatial data increases, the lack of efficient ways to find suitable information becomes critical. In this paper, a new methodology for knowledge discovery in geographic portals is presented. Based on the Semantic Web, our approach exploits the Resource Description Framework (RDF) in order to describe the geoportal's information with ontology-based metadata. When users traverse from page to page in the portal, they take advantage of the metadata infrastructure to navigate easily through data of interest. New metadata descriptions are published in the geoportal according to the RDF schemas.

  11. PERFORMANCE EVALUATION OF AN ALTERNATIVE CONTROLLER FOR BLUETOOTH SERVICE DISCOVERY

    Directory of Open Access Journals (Sweden)

    M. Sughasiny

    2012-06-01

    Full Text Available Bluetooth is a short range radio technology to form a small wireless system. It is used in low –cost, low power ad-hoc networks and it suffers from long service discovery delay and high power consumption. Bluetooth employs the 2.4 GHz ISM band, sharing the same bandwidth with the wireless LAN implementing the IEEE 802.11 standards. Thus it causes significantly lower interference. For improving the efficiency of SDP, we present an implementation of Bluetooth 2.1 in the NS-2 simulator, discuss the IEEE 802.11b as a Bluetooth controller and propose a new alternative Bluetooth Controller based on Adaptive Frequency Hopping techniques using Amplifier Power. The resulting approach significantly reduces the service discovery time, thereby lowering power consumption and increasing the throughput. We present the benefits of our new approach and compare it with existing approach using NS-2 Simulations and we have presented the comparison graphs in support of our approach.

  12. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol.

    Directory of Open Access Journals (Sweden)

    Fei Lu

    Full Text Available Switchgrass (Panicum virgatum L. is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS and genomic selection (GS. All of the 840 individuals were then genotyped using genotyping by sequencing (GBS, generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK. Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L. P. Beauv., two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics

  13. Systems pharmacology-based drug discovery for marine resources: an example using sea cucumber (Holothurians).

    Science.gov (United States)

    Guo, Yingying; Ding, Yan; Xu, Feifei; Liu, Baoyue; Kou, Zinong; Xiao, Wei; Zhu, Jingbo

    2015-05-13

    Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary. In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis. As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways. This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  14. ACFIS: a web server for fragment-based drug discovery.

    Science.gov (United States)

    Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

    2016-07-08

    In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Computational and Experimental Approaches to Cancer Biomarker Discovery

    DEFF Research Database (Denmark)

    Krzystanek, Marcin

    of a patient’s response to a particular treatment, thus helping to avoid unnecessary treatment and unwanted side effects in non-responding individuals.Currently biomarker discovery is facilitated by recent advances in high-throughput technologies when association between a given biological phenotype...... and the state or level of a large number of molecular entities is investigated. Such associative analysis could be confounded by several factors, leading to false discoveries. For example, it is assumed that with the exception of the true biomarkers most molecular entities such as gene expression levels show...... random distribution in a given cohort. However, gene expression levels may also be affected by technical bias when the actual measurement technology or sample handling may introduce a systematic error. If the distribution of systematic errors correlates with the biological phenotype then the risk...

  16. Price Discovery from Cross-Currency and FX Swaps: A Structural Analysis

    OpenAIRE

    Yasuaki Amatatsu; Naohiko Baba

    2007-01-01

    This paper investigates the relative role of price discovery between two long-term swap contracts that exchange between the U.S. dollar and the Japanese yen: cross-currency basis swap and FX (foreign exchange) swap. First, we show that these two swaps should be in a no-arbitrage relationship by allowing for differential risk premiums. Second, we empirically investigate the relative role of price discovery using the structural-form approach based on the state space models. Main finding are as ...

  17. Melodic pattern discovery by structural analysis via wavelets and clustering techniques

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David

    We present an automatic method to support melodic pattern discovery by structural analysis of symbolic representations by means of wavelet analysis and clustering techniques. In previous work, we used the method to recognize the parent works of melodic segments, or to classify tunes into tune......-means to cluster melodic segments into groups of measured similarity and obtain a raking of the most prototypical melodic segments or patterns and their occurrences. We test the method on the JKU Patterns Development Database and evaluate it based on the ground truth defined by the MIREX 2013 Discovery of Repeated...... Themes & Sections task. We compare the results of our method to the output of geometric approaches. Finally, we discuss about the relevance of our wavelet-based analysis in relation to structure, pattern discovery, similarity and variation, and comment about the considerations of the method when used...

  18. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

    Directory of Open Access Journals (Sweden)

    Li Min

    2012-03-01

    Full Text Available Abstract Background Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC, Betweenness Centrality (BC, Closeness Centrality (CC, Subgraph Centrality (SC, Eigenvector Centrality (EC, Information Centrality (IC, Bottle Neck (BN, Density of Maximum Neighborhood Component (DMNC, Local Average Connectivity-based method (LAC, Sum of ECC (SoECC, Range-Limited Centrality (RL, L-index (LI, Leader Rank (LR, Normalized α-Centrality (NC, and Moduland-Centrality (MC. Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN is more than 50% when predicting no more than 500 proteins. Conclusions We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

  19. The effect of discovery learning and problem-based learning on middle school students’ self-regulated learning

    Science.gov (United States)

    Miatun, A.; Muntazhimah

    2018-01-01

    The aim of this research was to determine the effect of learning models on mathematics achievement viewed from student’s self-regulated learning. The learning model compared were discovery learning and problem-based learning. The population was all students at the grade VIII of Junior High School in Boyolali regency. The samples were students of SMPN 4 Boyolali, SMPN 6 Boyolali, and SMPN 4 Mojosongo. The instruments used were mathematics achievement tests and self-regulated learning questionnaire. The data were analyzed using unbalanced two-ways Anova. The conclusion was as follows: (1) discovery learning gives better achievement than problem-based learning. (2) Achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. (3) For discovery learning, achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. For problem-based learning, students who have high and medium self-regulated learning have the same achievement. (4) For students who have high self-regulated learning, discovery learning gives better achievement than problem-based learning. Students who have medium and low self-regulated learning, both learning models give the same achievement.

  20. Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery.

    Science.gov (United States)

    Hall, David R; Kozakov, Dima; Whitty, Adrian; Vajda, Sandor

    2015-11-01

    Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Microarray-based ultra-high resolution discovery of genomic deletion mutations

    Science.gov (United States)

    2014-01-01

    Background Oligonucleotide microarray-based comparative genomic hybridization (CGH) offers an attractive possible route for the rapid and cost-effective genome-wide discovery of deletion mutations. CGH typically involves comparison of the hybridization intensities of genomic DNA samples with microarray chip representations of entire genomes, and has widespread potential application in experimental research and medical diagnostics. However, the power to detect small deletions is low. Results Here we use a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection. We show that the power to detect smaller deletions (4, 28 and 104 bp) depends upon oligonucleotide density (essentially the number of genome-representative oligonucleotides on the microarray chip), and determine the oligonucleotide spacings necessary to guarantee detection of deletions of specified size. Conclusions Our findings will enhance a wide range of research and clinical applications, and in particular will aid in the discovery of genomic deletions in the absence of a priori knowledge of their existence. PMID:24655320

  2. Synthetic biology of antimicrobial discovery

    Science.gov (United States)

    Zakeri, Bijan; Lu, Timothy K.

    2012-01-01

    Antibiotic discovery has a storied history. From the discovery of penicillin by Sir Alexander Fleming to the relentless quest for antibiotics by Selman Waksman, the stories have become like folklore, used to inspire future generations of scientists. However, recent discovery pipelines have run dry at a time when multidrug resistant pathogens are on the rise. Nature has proven to be a valuable reservoir of antimicrobial agents, which are primarily produced by modularized biochemical pathways. Such modularization is well suited to remodeling by an interdisciplinary approach that spans science and engineering. Herein, we discuss the biological engineering of small molecules, peptides, and non-traditional antimicrobials and provide an overview of the growing applicability of synthetic biology to antimicrobials discovery. PMID:23654251

  3. Relay discovery and selection for large-scale P2P streaming.

    Directory of Open Access Journals (Sweden)

    Chengwei Zhang

    Full Text Available In peer-to-peer networks, application relays have been commonly used to provide various networking services. The service performance often improves significantly if a relay is selected appropriately based on its network location. In this paper, we studied the location-aware relay discovery and selection problem for large-scale P2P streaming networks. In these large-scale and dynamic overlays, it incurs significant communication and computation cost to discover a sufficiently large relay candidate set and further to select one relay with good performance. The network location can be measured directly or indirectly with the tradeoffs between timeliness, overhead and accuracy. Based on a measurement study and the associated error analysis, we demonstrate that indirect measurements, such as King and Internet Coordinate Systems (ICS, can only achieve a coarse estimation of peers' network location and those methods based on pure indirect measurements cannot lead to a good relay selection. We also demonstrate that there exists significant error amplification of the commonly used "best-out-of-K" selection methodology using three RTT data sets publicly available. We propose a two-phase approach to achieve efficient relay discovery and accurate relay selection. Indirect measurements are used to narrow down a small number of high-quality relay candidates and the final relay selection is refined based on direct probing. This two-phase approach enjoys an efficient implementation using the Distributed-Hash-Table (DHT. When the DHT is constructed, the node keys carry the location information and they are generated scalably using indirect measurements, such as the ICS coordinates. The relay discovery is achieved efficiently utilizing the DHT-based search. We evaluated various aspects of this DHT-based approach, including the DHT indexing procedure, key generation under peer churn and message costs.

  4. Daily life activity routine discovery in hemiparetic rehabilitation patients using topic models.

    Science.gov (United States)

    Seiter, J; Derungs, A; Schuster-Amft, C; Amft, O; Tröster, G

    2015-01-01

    based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines. Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines. Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.

  5. AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

    International Nuclear Information System (INIS)

    Tsai, Yingssu; McPhillips, Scott E.; González, Ana; McPhillips, Timothy M.; Zinn, Daniel; Cohen, Aina E.; Feese, Michael D.; Bushnell, David; Tiefenbrunn, Theresa; Stout, C. David; Ludaescher, Bertram; Hedman, Britt; Hodgson, Keith O.; Soltis, S. Michael

    2013-01-01

    New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully

  6. Context based service discovery in unmanaged networks using MDNS/DNS-SD

    NARCIS (Netherlands)

    Stolikj, M.; Cuijpers, P.J.L.; Lukkien, J.J.; Buchina, N.; Bellido, F.J.; Vun, N.C.H.; Dolar, C.; Diaz-Sanchez, D.; Ling, W.-K.

    2016-01-01

    We propose an extension of the mDNS/DNS-SD service discovery protocol, which enables service clients to discover and select services based on their context. The extension improves scalability in large networks, which is of particular importance in future Internet of Things deployments.

  7. Discovery of pyrazolo[1,5-a]pyrimidine-based CHK1 inhibitors: A template-based approach-Part 2

    Energy Technology Data Exchange (ETDEWEB)

    Labroli, Marc; Paruch, Kamil; Dwyer, Michael P.; Alvarez, Carmen; Keertikar, Kartik; Poker, Cory; Rossman, Randall; Duca, Jose S.; Fischmann, Thierry O.; Madison, Vincent; Parry, David; Davis, Nicole; Seghezzi, Wolfgang; Wiswell, Derek; Guzi, Timothy J. [Merck

    2013-11-20

    Previous efforts by our group have established pyrazolo[1,5-a]pyrimidine as a viable core for the development of potent and selective CDK inhibitors. As part of an effort to utilize the pyrazolo[1,5-a]pyrimidine core as a template for the design and synthesis of potent and selective kinase inhibitors, we focused on a key regulator in the cell cycle progression, CHK1. Continued SAR development of the pyrazolo[1,5-a]pyrimidine core at the C5 and C6 positions, in conjunction with previously disclosed SAR at the C3 and C7 positions, led to the discovery of potent and selective CHK1 inhibitors.

  8. Knowledge discovery with classification rules in a cardiovascular dataset.

    Science.gov (United States)

    Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

    2005-12-01

    In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

  9. Cancer Biomarker Discovery: Lectin-Based Strategies Targeting Glycoproteins

    Directory of Open Access Journals (Sweden)

    David Clark

    2012-01-01

    Full Text Available Biomarker discovery can identify molecular markers in various cancers that can be used for detection, screening, diagnosis, and monitoring of disease progression. Lectin-affinity is a technique that can be used for the enrichment of glycoproteins from a complex sample, facilitating the discovery of novel cancer biomarkers associated with a disease state.

  10. An explanation of resisted discoveries based on construal-level theory.

    Science.gov (United States)

    Fang, Hui

    2015-02-01

    New discoveries and theories are crucial for the development of science, but they are often initially resisted by the scientific community. This paper analyses resistance to scientific discoveries that supplement previous research results or conclusions with new phenomena, such as long chains in macromolecules, Alfvén waves, parity nonconservation in weak interactions and quasicrystals. Construal-level theory is used to explain that the probability of new discoveries may be underestimated because of psychological distance. Thus, the insufficiently examined scope of an accepted theory may lead to overstating the suitable scope and underestimating the probability of its undiscovered counter-examples. Therefore, psychological activity can result in people instinctively resisting new discoveries. Direct evidence can help people judge the validity of a hypothesis with rational thinking. The effects of authorities and textbooks on the resistance to discoveries are also discussed. From the results of our analysis, suggestions are provided to reduce resistance to real discoveries, which will benefit the development of science.

  11. De Novo Discovery of Structured ncRNA Motifs in Genomic Sequences

    DEFF Research Database (Denmark)

    Ruzzo, Walter L; Gorodkin, Jan

    2014-01-01

    De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphas...... on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.......De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis...

  12. Comparison between project-based learning and discovery learning toward students' metacognitive strategies on global warming concept

    Science.gov (United States)

    Tumewu, Widya Anjelia; Wulan, Ana Ratna; Sanjaya, Yayan

    2017-05-01

    The purpose of this study was to know comparing the effectiveness of learning using Project-based learning (PjBL) and Discovery Learning (DL) toward students metacognitive strategies on global warming concept. A quasi-experimental research design with a The Matching-Only Pretest-Posttest Control Group Design was used in this study. The subjects were students of two classes 7th grade of one of junior high school in Bandung City, West Java of 2015/2016 academic year. The study was conducted on two experimental class, that were project-based learning treatment on the experimental class I and discovery learning treatment was done on the experimental class II. The data was collected through questionnaire to know students metacognitive strategies. The statistical analysis showed that there were statistically significant differences in students metacognitive strategies between project-based learning and discovery learning.

  13. Chemogenomic discovery of allosteric antagonists at the GPRC6A receptor

    DEFF Research Database (Denmark)

    Gloriam, David E.; Wellendorph, Petrine; Johansen, Lars Dan

    2011-01-01

    and pharmacological character: (1) chemogenomic lead identification through the first, to our knowledge, ligand inference between two different GPCR families, Families A and C; and (2) the discovery of the most selective GPRC6A allosteric antagonists discovered to date. The unprecedented inference of...... pharmacological activity across GPCR families provides proof-of-concept for in silico approaches against Family C targets based on Family A templates, greatly expanding the prospects of successful drug design and discovery. The antagonists were tested against a panel of seven Family A and C G protein-coupled receptors...

  14. Controlling the Rate of GWAS False Discoveries.

    Science.gov (United States)

    Brzyski, Damian; Peterson, Christine B; Sobczyk, Piotr; Candès, Emmanuel J; Bogdan, Malgorzata; Sabatti, Chiara

    2017-01-01

    With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study. Copyright © 2017 by the Genetics Society of America.

  15. Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development.

    Science.gov (United States)

    Bhardwaj, Anshu; Scaria, Vinod; Raghava, Gajendra Pal Singh; Lynn, Andrew Michael; Chandra, Nagasuma; Banerjee, Sulagna; Raghunandanan, Muthukurussi V; Pandey, Vikas; Taneja, Bhupesh; Yadav, Jyoti; Dash, Debasis; Bhattacharya, Jaijit; Misra, Amit; Kumar, Anil; Ramachandran, Srinivasan; Thomas, Zakir; Brahmachari, Samir K

    2011-09-01

    It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery. Copyright © 2011 Elsevier Ltd. All rights reserved.

  16. Solutions for wood-based bio-energy price discovery

    Energy Technology Data Exchange (ETDEWEB)

    Teraes, Timo [FOEX Indexes Ltd., Helsinki (Finland)], e-mail: timo@foex.fi

    2012-11-01

    Energy prices are highly volatile. This volatility can have serious ill-effects on the profitability of companies engaged in the energy business. There are, however, a number of price risk management tools which can be used to reduce the problems caused by price volatility. International trade of wood pellets and wood chips is rapidly growing. A good price transparency helps in developing the trade further. In order to meet the renewable energy targets within the EU, further growth of volumes is needed, at least within Europe and from overseas supply sources to the European markets. Reliable price indices are a central element in price risk management and in general price discovery. Exchanges have provided, in the past, the most widely known price discovery systems. Since 1990's, an increasing number of price risk management tools has been based on cash settlement concept. Cash settlement requires high quality benchmark price indices. These have been developed by the exchanges themselves, by trade press and by independent price benchmark provider companies. The best known of these benchmarks in forest industry and now also in wood-based bioenergy products are the PIX indices, provided by FOEX Indexes Ltd. This presentation discusses the key requirements for a good price index and the different ways of using the indices. Price relationships between wood chip prices and pellet prices are also discussed as will be the outlook for the future volume growth and trade flows in woodchips and pellets mainly from the European perspective.

  17. Location Discovery Based on Fuzzy Geometry in Passive Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rui Wang

    2011-01-01

    Full Text Available Location discovery with uncertainty using passive sensor networks in the nation's power grid is known to be challenging, due to the massive scale and inherent complexity. For bearings-only target localization in passive sensor networks, the approach of fuzzy geometry is introduced to investigate the fuzzy measurability for a moving target in R2 space. The fuzzy analytical bias expressions and the geometrical constraints are derived for bearings-only target localization. The interplay between fuzzy geometry of target localization and the fuzzy estimation bias for the case of fuzzy linear observer trajectory is analyzed in detail in sensor networks, which can realize the 3-dimensional localization including fuzzy estimate position and velocity of the target by measuring the fuzzy azimuth angles at intervals of fixed time. Simulation results show that the resulting estimate position outperforms the traditional least squares approach for localization with uncertainty.

  18. Computational methods in drug discovery

    Directory of Open Access Journals (Sweden)

    Sumudu P. Leelananda

    2016-12-01

    Full Text Available The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

  19. Toxins and drug discovery.

    Science.gov (United States)

    Harvey, Alan L

    2014-12-15

    Components from venoms have stimulated many drug discovery projects, with some notable successes. These are briefly reviewed, from captopril to ziconotide. However, there have been many more disappointments on the road from toxin discovery to approval of a new medicine. Drug discovery and development is an inherently risky business, and the main causes of failure during development programmes are outlined in order to highlight steps that might be taken to increase the chances of success with toxin-based drug discovery. These include having a clear focus on unmet therapeutic needs, concentrating on targets that are well-validated in terms of their relevance to the disease in question, making use of phenotypic screening rather than molecular-based assays, and working with development partners with the resources required for the long and expensive development process. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

  20. On the impact of network dynamics on a discovery protocol for ad-hoc networks

    NARCIS (Netherlands)

    Liu, F.; Heijenk, Geert

    A very promising approach to discovering services and context information in ad-hoc networks is based on the use of Attenuated Bloom filters. In this paper we analyze the impact of changes in the connectivity of an ad-hoc network on this approach. We evaluate the performance of the discovery

  1. Accounting for discovery bias in genomic prediction

    Science.gov (United States)

    Our objective was to evaluate an approach to mitigating discovery bias in genomic prediction. Accuracy may be improved by placing greater emphasis on regions of the genome expected to be more influential on a trait. Methods emphasizing regions result in a phenomenon known as “discovery bias” if info...

  2. Novel approaches to develop community-built biological network models for potential drug discovery.

    Science.gov (United States)

    Talikka, Marja; Bukharov, Natalia; Hayes, William S; Hofmann-Apitius, Martin; Alexopoulos, Leonidas; Peitsch, Manuel C; Hoeng, Julia

    2017-08-01

    Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

  3. Bioinformatics in translational drug discovery.

    Science.gov (United States)

    Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

    2017-08-31

    Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

  4. A Semantic Lexicon-Based Approach for Sense Disambiguation and Its WWW Application

    Science.gov (United States)

    di Lecce, Vincenzo; Calabrese, Marco; Soldo, Domenico

    This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts.

  5. Flexible End2End Workflow Automation of Hit-Discovery Research.

    Science.gov (United States)

    Holzmüller-Laue, Silke; Göde, Bernd; Thurow, Kerstin

    2014-08-01

    The article considers a new approach of more complex laboratory automation at the workflow layer. The authors purpose the automation of end2end workflows. The combination of all relevant subprocesses-whether automated or manually performed, independently, and in which organizational unit-results in end2end processes that include all result dependencies. The end2end approach focuses on not only the classical experiments in synthesis or screening, but also on auxiliary processes such as the production and storage of chemicals, cell culturing, and maintenance as well as preparatory activities and analyses of experiments. Furthermore, the connection of control flow and data flow in the same process model leads to reducing of effort of the data transfer between the involved systems, including the necessary data transformations. This end2end laboratory automation can be realized effectively with the modern methods of business process management (BPM). This approach is based on a new standardization of the process-modeling notation Business Process Model and Notation 2.0. In drug discovery, several scientific disciplines act together with manifold modern methods, technologies, and a wide range of automated instruments for the discovery and design of target-based drugs. The article discusses the novel BPM-based automation concept with an implemented example of a high-throughput screening of previously synthesized compound libraries. © 2014 Society for Laboratory Automation and Screening.

  6. Graph-Based Methods for Discovery Browsing with Semantic Predications

    DEFF Research Database (Denmark)

    Wilkowski, Bartlomiej; Fiszman, Marcelo; Miller, Christopher M

    2011-01-01

    . Poorly understood relationships may be explored through novel points of view, and potentially interesting relationships need not be known ahead of time. In a process of "cooperative reciprocity" the user iteratively focuses system output, thus controlling the large number of relationships often generated...... in literature-based discovery systems. The underlying technology exploits SemRep semantic predications represented as a graph of interconnected nodes (predication arguments) and edges (predicates). The system suggests paths in this graph, which represent chains of relationships. The methodology is illustrated...

  7. Data mining-aided materials discovery and optimization

    Directory of Open Access Journals (Sweden)

    Wencong Lu

    2017-09-01

    Full Text Available Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper, and an introduction to the materials data mining (MDM process is provided using case studies. Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery, design, and optimization. State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co3O4 superstructures, materials design of layered double hydroxide, battery materials discovery, and thermoelectric materials design. The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation, and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.

  8. Dose selection based on physiologically based pharmacokinetic (PBPK) approaches.

    Science.gov (United States)

    Jones, Hannah M; Mayawala, Kapil; Poulin, Patrick

    2013-04-01

    Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.

  9. Native Mass Spectrometry in Fragment-Based Drug Discovery

    Directory of Open Access Journals (Sweden)

    Liliana Pedro

    2016-07-01

    Full Text Available The advent of native mass spectrometry (MS in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein–ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD. Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

  10. Native Mass Spectrometry in Fragment-Based Drug Discovery.

    Science.gov (United States)

    Pedro, Liliana; Quinn, Ronald J

    2016-07-28

    The advent of native mass spectrometry (MS) in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein-ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD). Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

  11. Predicting future discoveries from current scientific literature.

    Science.gov (United States)

    Petrič, Ingrid; Cestnik, Bojan

    2014-01-01

    Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

  12. Symbiosis-inspired approaches to antibiotic discovery.

    Science.gov (United States)

    Adnani, Navid; Rajski, Scott R; Bugni, Tim S

    2017-07-06

    Covering: 2010 up to 2017Life on Earth is characterized by a remarkable abundance of symbiotic and highly refined relationships among life forms. Defined as any kind of close, long-term association between two organisms, symbioses can be mutualistic, commensalistic or parasitic. Historically speaking, selective pressures have shaped symbioses in which one organism (typically a bacterium or fungus) generates bioactive small molecules that impact the host (and possibly other symbionts); the symbiosis is driven fundamentally by the genetic machineries available to the small molecule producer. The human microbiome is now integral to the most recent chapter in animal-microbe symbiosis studies and plant-microbe symbioses have significantly advanced our understanding of natural products biosynthesis; this also is the case for studies of fungal-microbe symbioses. However, much less is known about microbe-microbe systems involving interspecies interactions. Microbe-derived small molecules (i.e. antibiotics and quorum sensing molecules, etc.) have been shown to regulate transcription in microbes within the same environmental niche, suggesting interspecies interactions whereas, intraspecies interactions, such as those that exploit autoinducing small molecules, also modulate gene expression based on environmental cues. We, and others, contend that symbioses provide almost unlimited opportunities for the discovery of new bioactive compounds whose activities and applications have been evolutionarily optimized. Particularly intriguing is the possibility that environmental effectors can guide laboratory expression of secondary metabolites from "orphan", or silent, biosynthetic gene clusters (BGCs). Notably, many of the studies summarized here result from advances in "omics" technologies and highlight how symbioses have given rise to new anti-bacterial and antifungal natural products now being discovered.

  13. Object-graphs for context-aware visual category discovery.

    Science.gov (United States)

    Lee, Yong Jae; Grauman, Kristen

    2012-02-01

    How can knowing about some categories help us to discover new ones in unlabeled images? Unsupervised visual category discovery is useful to mine for recurring objects without human supervision, but existing methods assume no prior information and thus tend to perform poorly for cluttered scenes with multiple objects. We propose to leverage knowledge about previously learned categories to enable more accurate discovery, and address challenges in estimating their familiarity in unsegmented, unlabeled images. We introduce two variants of a novel object-graph descriptor to encode the 2D and 3D spatial layout of object-level co-occurrence patterns relative to an unfamiliar region and show that by using them to model the interaction between an image’s known and unknown objects, we can better detect new visual categories. Rather than mine for all categories from scratch, our method identifies new objects while drawing on useful cues from familiar ones. We evaluate our approach on several benchmark data sets and demonstrate clear improvements in discovery over conventional purely appearance-based baselines.

  14. Direct: Ontology based discovery of responsibility and causality in legal case descriptions

    NARCIS (Netherlands)

    Breuker, J.A.P.J.; Hoekstra, R.J.; Gordon, T.

    2004-01-01

    In this paper we present DIRECT, a system forautomatic discovery of responsibility and causal relations in legal case descriptions based on LRI-Core, a core ontology that covers the main concepts that are common to all legal domains. These domains have a predominant common-sense character - the law

  15. The Spiral Discovery Network as an Automated General-Purpose Optimization Tool

    Directory of Open Access Journals (Sweden)

    Adam B. Csapo

    2018-01-01

    Full Text Available The Spiral Discovery Method (SDM was originally proposed as a cognitive artifact for dealing with black-box models that are dependent on multiple inputs with nonlinear and/or multiplicative interaction effects. Besides directly helping to identify functional patterns in such systems, SDM also simplifies their control through its characteristic spiral structure. In this paper, a neural network-based formulation of SDM is proposed together with a set of automatic update rules that makes it suitable for both semiautomated and automated forms of optimization. The behavior of the generalized SDM model, referred to as the Spiral Discovery Network (SDN, and its applicability to nondifferentiable nonconvex optimization problems are elucidated through simulation. Based on the simulation, the case is made that its applicability would be worth investigating in all areas where the default approach of gradient-based backpropagation is used today.

  16. The Discovery of Aurora Kinase Inhibitor by Multi-Docking-Based Virtual Screening

    Directory of Open Access Journals (Sweden)

    Jun-Tae Kim

    2014-11-01

    Full Text Available We report the discovery of aurora kinase inhibitor using the fragment-based virtual screening by multi-docking strategy. Among a number of fragments collected from eMololecules, we found four fragment molecules showing potent activity (>50% at 100 μM against aurora kinase. Based on the explored fragment scaffold, we selected two compounds in our synthesized library and validated the biological activity against Aurora kinase.

  17. Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia.

    Science.gov (United States)

    Keserű, György M; Erlanson, Daniel A; Ferenczy, György G; Hann, Michael M; Murray, Christopher W; Pickett, Stephen D

    2016-09-22

    Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation, and screening technologies. This compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia.

  18. Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

    Science.gov (United States)

    Abdolmaleki, Azizeh; Ghasemi, Jahan B

    2017-01-01

    Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Viral pathogen discovery

    Science.gov (United States)

    Chiu, Charles Y

    2015-01-01

    Viral pathogen discovery is of critical importance to clinical microbiology, infectious diseases, and public health. Genomic approaches for pathogen discovery, including consensus polymerase chain reaction (PCR), microarrays, and unbiased next-generation sequencing (NGS), have the capacity to comprehensively identify novel microbes present in clinical samples. Although numerous challenges remain to be addressed, including the bioinformatics analysis and interpretation of large datasets, these technologies have been successful in rapidly identifying emerging outbreak threats, screening vaccines and other biological products for microbial contamination, and discovering novel viruses associated with both acute and chronic illnesses. Downstream studies such as genome assembly, epidemiologic screening, and a culture system or animal model of infection are necessary to establish an association of a candidate pathogen with disease. PMID:23725672

  20. Incorporation of rapid thermodynamic data in fragment-based drug discovery.

    Science.gov (United States)

    Kobe, Akihiro; Caaveiro, Jose M M; Tashiro, Shinya; Kajihara, Daisuke; Kikkawa, Masato; Mitani, Tomoya; Tsumoto, Kouhei

    2013-03-14

    Fragment-based drug discovery (FBDD) has enjoyed increasing popularity in recent years. We introduce SITE (single-injection thermal extinction), a novel thermodynamic methodology that selects high-quality hits early in FBDD. SITE is a fast calorimetric competitive assay suitable for automation that captures the essence of isothermal titration calorimetry but using significantly fewer resources. We describe the principles of SITE and identify a novel family of fragment inhibitors of the enzyme ketosteroid isomerase displaying high values of enthalpic efficiency.

  1. From Mollusks to Medicine: A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins

    Directory of Open Access Journals (Sweden)

    Aida Verdes

    2016-04-01

    Full Text Available Animal venoms comprise a diversity of peptide toxins that manipulate molecular targets such as ion channels and receptors, making venom peptides attractive candidates for the development of therapeutics to benefit human health. However, identifying bioactive venom peptides remains a significant challenge. In this review we describe our particular venomics strategy for the discovery, characterization, and optimization of Terebridae venom peptides, teretoxins. Our strategy reflects the scientific path from mollusks to medicine in an integrative sequential approach with the following steps: (1 delimitation of venomous Terebridae lineages through taxonomic and phylogenetic analyses; (2 identification and classification of putative teretoxins through omics methodologies, including genomics, transcriptomics, and proteomics; (3 chemical and recombinant synthesis of promising peptide toxins; (4 structural characterization through experimental and computational methods; (5 determination of teretoxin bioactivity and molecular function through biological assays and computational modeling; (6 optimization of peptide toxin affinity and selectivity to molecular target; and (7 development of strategies for effective delivery of venom peptide therapeutics. While our research focuses on terebrids, the venomics approach outlined here can be applied to the discovery and characterization of peptide toxins from any venomous taxa.

  2. Proteome analysis of body fluids for amyotrophic lateral sclerosis biomarker discovery.

    Science.gov (United States)

    Krüger, Thomas; Lautenschläger, Janin; Grosskreutz, Julian; Rhode, Heidrun

    2013-01-01

    Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder of motor neurons leading to death of the patients, mostly within 2-5 years after disease onset. The pathomechanism of motor neuron degeneration is only partially understood and therapeutic strategies based on mechanistic insights are largely ineffective. The discovery of reliable biomarkers of disease diagnosis and progression is the sine qua non of both the revelation of insights into the ALS pathomechanism and the assessment of treatment efficacies. Proteomic approaches are an important pillar in ALS biomarker discovery. Cerebrospinal fluid is the most promising body fluid for differential proteome analyses, followed by blood (serum, plasma), and even urine and saliva. The present study provides an overview about reported peptide/protein biomarker candidates that showed significantly altered levels in certain body fluids of ALS patients. These findings have to be discussed according to proposed pathomechanisms to identify modifiers of disease progression and to pave the way for the development of potential therapeutic strategies. Furthermore, limitations and advantages of proteomic approaches for ALS biomarker discovery in different body fluids and reliable validation of biomarker candidates have been addressed. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Developing computer-based training programs for basic mammalian histology: Didactic versus discovery-based design

    Science.gov (United States)

    Fabian, Henry Joel

    Educators have long tried to understand what stimulates students to learn. The Swiss psychologist and zoologist, Jean Claude Piaget, suggested that students are stimulated to learn when they attempt to resolve confusion. He reasoned that students try to explain the world with the knowledge they have acquired in life. When they find their own explanations to be inadequate to explain phenomena, students find themselves in a temporary state of confusion. This prompts students to seek more plausible explanations. At this point, students are primed for learning (Piaget 1964). The Piagetian approach described above is called learning by discovery. To promote discovery learning, a teacher must first allow the student to recognize his misconception and then provide a plausible explanation to replace that misconception (Chinn and Brewer 1993). One application of this method is found in the various learning cycles, which have been demonstrated to be effective means for teaching science (Renner and Lawson 1973, Lawson 1986, Marek and Methven 1991, and Glasson & Lalik 1993). In contrast to the learning cycle, tutorial computer programs are generally not designed to correct student misconceptions, but rather follow a passive, didactic method of teaching. In the didactic or expositional method, the student is told about a phenomenon, but is neither encouraged to explore it, nor explain it in his own terms (Schneider and Renner 1980).

  4. NASA Reverb: Standards-Driven Earth Science Data and Service Discovery

    Science.gov (United States)

    Cechini, M. F.; Mitchell, A.; Pilone, D.

    2011-12-01

    . After a yearlong design, development, and testing process, the ECHO team successfully released "Reverb - The Next Generation Earth Science Discovery Tool." Reverb relies heavily on the information contained in dataset and granule metadata, such as ISO 19115, to provide a dynamic experience to users based on identified search facet values extracted from science metadata. Such an approach allows users to perform cross-dataset correlation and searches, discovering additional data that they may not previously have been aware of. In addition to data discovery, Reverb users may discover services associated with their data of interest. When services utilize supported standards and/or protocols, Reverb can facilitate the invocation of both synchronous and asynchronous data processing services. This greatly enhances a users ability to discover data of interest and accomplish their research goals. Extrapolating on the current movement towards interoperable standards and an increase in available services, data service invocation and chaining will become a natural part of data discovery. Reverb is one example of a discovery tool that provides a mechanism for transforming the earth science data discovery paradigm.

  5. Genome engineering for microbial natural product discovery.

    Science.gov (United States)

    Choi, Si-Sun; Katsuyama, Yohei; Bai, Linquan; Deng, Zixin; Ohnishi, Yasuo; Kim, Eung-Soo

    2018-03-03

    The discovery and development of microbial natural products (MNPs) have played pivotal roles in the fields of human medicine and its related biotechnology sectors over the past several decades. The post-genomic era has witnessed the development of microbial genome mining approaches to isolate previously unsuspected MNP biosynthetic gene clusters (BGCs) hidden in the genome, followed by various BGC awakening techniques to visualize compound production. Additional microbial genome engineering techniques have allowed higher MNP production titers, which could complement a traditional culture-based MNP chasing approach. Here, we describe recent developments in the MNP research paradigm, including microbial genome mining, NP BGC activation, and NP overproducing cell factory design. Copyright © 2018 Elsevier Ltd. All rights reserved.

  6. Retrieval of criminal trajectories with an FCA-based approach

    NARCIS (Netherlands)

    Poelmans, J.; Elzinga, P.; Dedene, G.

    2013-01-01

    In this paper we briefly discuss the possibilities of Formal Concept Analysis for gaining insight in large amounts of unstructured police reports. We present a generic human centred knowledge discovery approach and showcase promising results obtained during empirical validation. The first case study

  7. HPV vaccines: their pathology-based discovery, benefits, and adverse effects.

    Science.gov (United States)

    Nicol, Alcina F; de Andrade, Cecilia V; Russomano, Fabio B; Rodrigues, Luana S L; Oliveira, Nathalia S; Provance, David William; Nuovo, Gerard J

    2015-12-01

    The discovery of the human papillomavirus (HPV) vaccine illustrates the power of in situ-based pathologic analysis in better understanding and curing diseases. The 2 available HPV vaccines have markedly reduced the incidence of cervical intraepithelial neoplasias, genital warts, and cervical cancer throughout the world. Concerns about HPV vaccine safety have led some physicians, health care officials, and parents to refuse providing the recommended vaccination to the target population. The aims of the study were to discuss the discovery of HPV vaccine and review scientific data related to measurable outcomes from the use of HPV vaccines. The strong type-specific immunity against HPV in humans has been known for more than 25 years. Multiple studies confirm the positive risk benefit of HPV vaccination with minimal documented adverse effects. The most common adverse effect, injection site pain, occurred in about 10% of girls and was less than the rate reported for other vaccines. Use of HPV vaccine should be expanded into more diverse populations, mainly in low-resource settings. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Therapeutic approaches to genetic ion channelopathies and perspectives in drug discovery

    Directory of Open Access Journals (Sweden)

    Paola eImbrici

    2016-05-01

    Full Text Available In the human genome more than 400 genes encode ion channels, which are transmembrane proteins mediating ion fluxes across membranes. Being expressed in all cell types, they are involved in almost all physiological processes, including sense perception, neurotransmission, muscle contraction, secretion, immune response, cell proliferation and differentiation. Due to the widespread tissue distribution of ion channels and their physiological functions, mutations in genes encoding ion channel subunits, or their interacting proteins, are responsible for inherited ion channelopathies. These diseases can range from common to very rare disorders and their severity can be mild, disabling, or life-threatening. In spite of this, ion channels are the primary target of only about 5% of the marketed drugs suggesting their potential in drug discovery. The current review summarizes the therapeutic management of the principal ion channelopathies of central and peripheral nervous system, heart, kidney, bone, skeletal muscle and pancreas, resulting from mutations in calcium, sodium, potassium and chloride ion channels. For most channelopathies the therapy is mainly empirical and symptomatic, often limited by lack of efficacy and tolerability for a significant number of patients. Other channelopathies can exploit ion channel targeted drugs, such as marketed sodium channel blockers. Developing new and more specific therapeutic approaches is therefore required. To this aim, a major advancement in the pharmacotherapy of channelopathies has been the discovery that ion channel mutations lead to change in biophysics that can in turn specifically modify the sensitivity to drugs: this opens the way to a pharmacogenetics strategy, allowing the development of a personalized therapy with increased efficacy and reduced side effects. In addition, the identification of disease modifiers in ion channelopathies appears an alternative strategy to discover novel druggable targets.

  9. Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data.

    Science.gov (United States)

    Mamykina, Lena; Heitkemper, Elizabeth M; Smaldone, Arlene M; Kukafka, Rita; Cole-Lewis, Heather J; Davidson, Patricia G; Mynatt, Elizabeth D; Cassells, Andrea; Tobin, Jonathan N; Hripcsak, George

    2017-12-01

    To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes. We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14). The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals' activities and changes in their blood glucose levels that the participants referred to as "cause and effect". This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors). The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can

  10. Geo-Enrichment and Semantic Enhancement of Metadata Sets to Augment Discovery in Geoportals

    Directory of Open Access Journals (Sweden)

    Bernhard Vockner

    2014-03-01

    Full Text Available Geoportals are established to function as main gateways to find, evaluate, and start “using” geographic information. Still, current geoportal implementations face problems in optimizing the discovery process due to semantic heterogeneity issues, which leads to low recall and low precision in performing text-based searches. Therefore, we propose an enhanced semantic discovery approach that supports multilingualism and information domain context. Thus, we present workflow that enriches existing structured metadata with synonyms, toponyms, and translated terms derived from user-defined keywords based on multilingual thesauri and ontologies. To make the results easier and understandable, we also provide automated translation capabilities for the resource metadata to support the user in conceiving the thematic content of the descriptive metadata, even if it has been documented using a language the user is not familiar with. In addition, to text-enable spatial filtering capabilities, we add additional location name keywords to metadata sets. These are based on the existing bounding box and shall tweak discovery scores when performing single text line queries. In order to improve the user’s search experience, we tailor faceted search strategies presenting an enhanced query interface for geo-metadata discovery that are transparently leveraging the underlying thesauri and ontologies.

  11. Genomics-Based Discovery of Plant Genes for Synthetic Biology of Terpenoid Fragrances: A Case Study in Sandalwood oil Biosynthesis.

    Science.gov (United States)

    Celedon, J M; Bohlmann, J

    2016-01-01

    Terpenoid fragrances are powerful mediators of ecological interactions in nature and have a long history of traditional and modern industrial applications. Plants produce a great diversity of fragrant terpenoid metabolites, which make them a superb source of biosynthetic genes and enzymes. Advances in fragrance gene discovery have enabled new approaches in synthetic biology of high-value speciality molecules toward applications in the fragrance and flavor, food and beverage, cosmetics, and other industries. Rapid developments in transcriptome and genome sequencing of nonmodel plant species have accelerated the discovery of fragrance biosynthetic pathways. In parallel, advances in metabolic engineering of microbial and plant systems have established platforms for synthetic biology applications of some of the thousands of plant genes that underlie fragrance diversity. While many fragrance molecules (eg, simple monoterpenes) are abundant in readily renewable plant materials, some highly valuable fragrant terpenoids (eg, santalols, ambroxides) are rare in nature and interesting targets for synthetic biology. As a representative example for genomics/transcriptomics enabled gene and enzyme discovery, we describe a strategy used successfully for elucidation of a complete fragrance biosynthetic pathway in sandalwood (Santalum album) and its reconstruction in yeast (Saccharomyces cerevisiae). We address questions related to the discovery of specific genes within large gene families and recovery of rare gene transcripts that are selectively expressed in recalcitrant tissues. To substantiate the validity of the approaches, we describe the combination of methods used in the gene and enzyme discovery of a cytochrome P450 in the fragrant heartwood of tropical sandalwood, responsible for the fragrance defining, final step in the biosynthesis of (Z)-santalols. © 2016 Elsevier Inc. All rights reserved.

  12. Shotgun Proteomics and Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    W. Hayes McDonald

    2002-01-01

    Full Text Available Coupling large-scale sequencing projects with the amino acid sequence information that can be gleaned from tandem mass spectrometry (MS/MS has made it much easier to analyze complex mixtures of proteins. The limits of this “shotgun” approach, in which the protein mixture is proteolytically digested before separation, can be further expanded by separating the resulting mixture of peptides prior to MS/MS analysis. Both single dimensional high pressure liquid chromatography (LC and multidimensional LC (LC/LC can be directly interfaced with the mass spectrometer to allow for automated collection of tremendous quantities of data. While there is no single technique that addresses all proteomic challenges, the shotgun approaches, especially LC/LC-MS/MS-based techniques such as MudPIT (multidimensional protein identification technology, show advantages over gel-based techniques in speed, sensitivity, scope of analysis, and dynamic range. Advances in the ability to quantitate differences between samples and to detect for an array of post-translational modifications allow for the discovery of classes of protein biomarkers that were previously unassailable.

  13. High-efficiency combinatorial approach as an effective tool for accelerating metallic biomaterials research and discovery

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, X.D. [School of Material Science and Engineering, Central South University, Changsha, Hunan, 410083 (China); Liu, L.B., E-mail: lbliu.csu@gmail.com [School of Material Science and Engineering, Central South University, Changsha, Hunan, 410083 (China); State Key Laboratory for Powder Metallurgy, Changsha, Hunan, 410083 (China); Zhao, J.-C. [State Key Laboratory for Powder Metallurgy, Changsha, Hunan, 410083 (China); Department of Materials Science and Engineering, The Ohio State University, 2041 College Road, Columbus, OH 43210 (United States); Wang, J.L.; Zheng, F.; Jin, Z.P. [School of Material Science and Engineering, Central South University, Changsha, Hunan, 410083 (China)

    2014-06-01

    A high-efficiency combinatorial approach has been applied to rapidly build the database of composition-dependent elastic modulus and hardness of the Ti–Ta and Ti–Zr–Ta systems. A diffusion multiple of the Ti–Zr–Ta system was manufactured, then annealed at 1173 K for 1800 h, and water quenched to room temperature. Extensive interdiffusion among Ti, Zr and Ta has taken place. Combining nanoindentation and electron probe micro-analysis (EPMA), the elastic modulus, hardness as well as composition across the diffusion multiple were determined. The composition/elastic modulus/hardness relationship of the Ti–Ta and Ti–Zr–Ta alloys has been obtained. It was found that the elastic modulus and hardness depend strongly on the Ta and Zr content. The result can be used to accelerate the discovery/development of bio-titanium alloys for different components in implant prosthesis. - Highlights: • High-efficiency diffusion multiple of Ti–Zr–Ta was manufactured. • Composition-dependent elastic modulus and hardness of the Ti–Ta and Ti–Zr–Ta systems have been obtained effectively, • The methodology and the information can be used to accelerate the discovery/development of bio-titanium alloys.

  14. Finding potentially new multimorbidity patterns of psychiatric and somatic diseases: exploring the use of literature-based discovery in primary care research.

    Science.gov (United States)

    Vos, Rein; Aarts, Sil; van Mulligen, Erik; Metsemakers, Job; van Boxtel, Martin P; Verhey, Frans; van den Akker, Marjan

    2014-01-01

    Multimorbidity, the co-occurrence of two or more chronic medical conditions within a single individual, is increasingly becoming part of daily care of general medical practice. Literature-based discovery may help to investigate the patterns of multimorbidity and to integrate medical knowledge for improving healthcare delivery for individuals with co-occurring chronic conditions. To explore the usefulness of literature-based discovery in primary care research through the key-case of finding associations between psychiatric and somatic diseases relevant to general practice in a large biomedical literature database (Medline). By using literature based discovery for matching disease profiles as vectors in a high-dimensional associative concept space, co-occurrences of a broad spectrum of chronic medical conditions were matched for their potential in biomedicine. An experimental setting was chosen in parallel with expert evaluations and expert meetings to assess performance and to generate targets for integrating literature-based discovery in multidisciplinary medical research of psychiatric and somatic disease associations. Through stepwise reductions a reference set of 21,945 disease combinations was generated, from which a set of 166 combinations between psychiatric and somatic diseases was selected and assessed by text mining and expert evaluation. Literature-based discovery tools generate specific patterns of associations between psychiatric and somatic diseases: one subset was appraised as promising for further research; the other subset surprised the experts, leading to intricate discussions and further eliciting of frameworks of biomedical knowledge. These frameworks enable us to specify targets for further developing and integrating literature-based discovery in multidisciplinary research of general practice, psychology and psychiatry, and epidemiology.

  15. A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

    Directory of Open Access Journals (Sweden)

    Michael Jae-Yoon Chung

    Full Text Available A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i learn probabilistic models of actions through self-discovery and experience, (ii utilize these learned models for inferring the goals of human actions, and (iii perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i a simulated robot that learns human-like gaze following behavior, and (ii a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

  16. Fragment-Based Discovery of Pyrimido[1,2-b]indazole PDE10A Inhibitors.

    Science.gov (United States)

    Chino, Ayaka; Seo, Ryushi; Amano, Yasushi; Namatame, Ichiji; Hamaguchi, Wataru; Honbou, Kazuya; Mihara, Takuma; Yamazaki, Mayako; Tomishima, Masaki; Masuda, Naoyuki

    2018-01-01

    In this study, we report the identification of potent pyrimidoindazoles as phosphodiesterase10A (PDE10A) inhibitors by using the method of fragment-based drug discovery (FBDD). The pyrazolopyridine derivative 2 was found to be a fragment hit compound which could occupy a part of the binding site of PDE10A enzyme by using the method of the X-ray co-crystal structure analysis. On the basis of the crystal structure of compound 2 and PDE10A protein, a number of compounds were synthesized and evaluated, by means of structure-activity relationship (SAR) studies, which culminated in the discovery of a novel pyrimidoindazole derivative 13 having good physicochemical properties.

  17. Big data analytics in immunology: a knowledge-based approach.

    Science.gov (United States)

    Zhang, Guang Lan; Sun, Jing; Chitkushev, Lou; Brusic, Vladimir

    2014-01-01

    With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

  18. Big Data Analytics in Immunology: A Knowledge-Based Approach

    Directory of Open Access Journals (Sweden)

    Guang Lan Zhang

    2014-01-01

    Full Text Available With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

  19. Array based Discovery of Aptamer Pairs (Open Access Publisher’s Version)

    Science.gov (United States)

    2014-12-11

    Array-based Discovery of Aptamer Pairs Minseon Cho,†,‡ Seung Soo Oh,‡ Jeff Nie,§ Ron Stewart,§ Monte J. Radeke,⊥ Michael Eisenstein ,†,‡ Peter J...ac504076k | Anal. Chem. 2015, 87, 821−828827 (24) Cho, M.; Oh, S. S.; Nie, J.; Stewart, R.; Eisenstein , M.; Chambers, J.; Marth, J. D.; Walker, F

  20. Natural product derived insecticides: discovery and development of spinetoram.

    Science.gov (United States)

    Galm, Ute; Sparks, Thomas C

    2016-03-01

    This review highlights the importance of natural product research and industrial microbiology for product development in the agricultural industry, based on examples from Dow AgroSciences. It provides an overview of the discovery and development of spinetoram, a semisynthetic insecticide derived by a combination of a genetic block in a specific O-methylation of the rhamnose moiety of spinosad coupled with neural network-based QSAR and synthetic chemistry. It also emphasizes the key role that new technologies and multidisciplinary approaches play in the development of current spinetoram production strains.

  1. From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

    Science.gov (United States)

    Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

    2017-11-01

    Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Combinatorial thin film materials science: From alloy discovery and optimization to alloy design

    Energy Technology Data Exchange (ETDEWEB)

    Gebhardt, Thomas, E-mail: gebhardt@mch.rwth-aachen.de; Music, Denis; Takahashi, Tetsuya; Schneider, Jochen M.

    2012-06-30

    This paper provides an overview of modern alloy development, from discovery and optimization towards alloy design, based on combinatorial thin film materials science. The combinatorial approach, combining combinatorial materials synthesis of thin film composition-spreads with high-throughput property characterization has proven to be a powerful tool to delineate composition-structure-property relationships, and hence to efficiently identify composition windows with enhanced properties. Furthermore, and most importantly for alloy design, theoretical models and hypotheses can be critically appraised. Examples for alloy discovery, optimization, and alloy design of functional as well as structural materials are presented. Using Fe-Mn based alloys as an example, we show that the combination of modern electronic-structure calculations with the highly efficient combinatorial thin film composition-spread method constitutes an effective tool for knowledge-based alloy design.

  3. Combinatorial thin film materials science: From alloy discovery and optimization to alloy design

    International Nuclear Information System (INIS)

    Gebhardt, Thomas; Music, Denis; Takahashi, Tetsuya; Schneider, Jochen M.

    2012-01-01

    This paper provides an overview of modern alloy development, from discovery and optimization towards alloy design, based on combinatorial thin film materials science. The combinatorial approach, combining combinatorial materials synthesis of thin film composition-spreads with high-throughput property characterization has proven to be a powerful tool to delineate composition–structure–property relationships, and hence to efficiently identify composition windows with enhanced properties. Furthermore, and most importantly for alloy design, theoretical models and hypotheses can be critically appraised. Examples for alloy discovery, optimization, and alloy design of functional as well as structural materials are presented. Using Fe-Mn based alloys as an example, we show that the combination of modern electronic-structure calculations with the highly efficient combinatorial thin film composition-spread method constitutes an effective tool for knowledge-based alloy design.

  4. PENGARUH PENDEKATAN DISCOVERY YANG MENEKANKAN ASPEK ANALOGI TERHADAP PRESTASI BELAJAR, KEMAMPUAN PENALARAN, KECERDASAN EMOSIONAL SPIRITUAL

    Directory of Open Access Journals (Sweden)

    Nur Choiro Siregar

    2015-11-01

    Full Text Available Penelitian ini bertujuan menyelidiki pengaruh pembelajaran segiempat dan segitiga dengan pendekatan discovery yang menekankan aspek analogi terhadap prestasi belajar, kemampuan penalaran, dan kecerdasan emosional spiritual siswa SMP Negeri 9 Yogyakarta. Penelitian ini merupakan penelitian eksperimen semu.Instrumen yang digunakan adalah tes prestasi belajar, tes kemampuan penalaran, dan angket kecerdasan emosional spiritual. Data dianalisis menggunakan uji Multivariate Analysis of Variance (Manova dan Analysis of Variance (Anova. Hasil penelitian menunjukkan ada pengaruh pembelajaran segiempat dan segitiga dengan pendekatan discovery yang menekankan aspek analogi terhadap prestasi belajar, dan kemampuan penalaran siswa. Berdasarkan analisis, pembelajaran segiempat dan segitiga dengan pendekatan discovery yang menekankan aspek analogi lebih unggul daripada pembelajaran biasa dalam hal prestasi belajar dan kemampuan penalaran. Sebaliknya, dalam hal kecerdasan emosional spiritual siswa, pendekatan discovery yang menekankan aspek analogi tidak memberi pengaruh dan tidak lebih unggul daripada pembelajaran biasa. Kata Kunci: discovery, menekankan aspek analogi, prestasi belajar, kemampuan penalaran, kecerdasan emosional spiritual   THE EFFECT OF DISCOVERY APPROACH EMPHASING ON THE ANALOGY ASPECT ON ACHIEVEMENT, REASONING ABILITY, EMOTIONAL SPIRITUAL INTELLIGENCE Abstract This study aims to investigate the effect of quadrilateral and triangle teaching using the disco-very approach emphasing on the analogy aspect on achievement, reasoning ability, and emotional spiritual intelligenceof Grade VII students of SMPN 9 Yogyakarta. This study was a quasi-experimen-tal study. The instruments of the study were an achievement test, reasoning ability test, and emotional spiritual intelligence questionnaire. The data wereanalyzed using the Multivariate Analysis of Variance (Manova and Analysis of Variance(Anova tests. The results of the study are as follows. There

  5. Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

    Directory of Open Access Journals (Sweden)

    Federica Villanova

    Full Text Available Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid flow cytometry platform (CFP and a unique lyoplate-based flow cytometry platform (LFP in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10 and activation markers (Foxp3 and CD25. Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

  6. Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

    Science.gov (United States)

    Villanova, Federica; Di Meglio, Paola; Inokuma, Margaret; Aghaeepour, Nima; Perucha, Esperanza; Mollon, Jennifer; Nomura, Laurel; Hernandez-Fuentes, Maria; Cope, Andrew; Prevost, A Toby; Heck, Susanne; Maino, Vernon; Lord, Graham; Brinkman, Ryan R; Nestle, Frank O

    2013-01-01

    Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

  7. Applying genetics in inflammatory disease drug discovery

    DEFF Research Database (Denmark)

    Folkersen, Lasse; Biswas, Shameek; Frederiksen, Klaus Stensgaard

    2015-01-01

    , with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide...... alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection....

  8. Drug discovery for Chagas disease should consider Trypanosoma cruzi strain diversity

    Directory of Open Access Journals (Sweden)

    Bianca Zingales

    2014-09-01

    Full Text Available This opinion piece presents an approach to standardisation of an important aspect of Chagas disease drug discovery and development: selecting Trypanosoma cruzi strains for in vitro screening. We discuss the rationale for strain selection representing T. cruzi diversity and provide recommendations on the preferred parasite stage for drug discovery, T. cruzi discrete typing units to include in the panel of strains and the number of strains/clones for primary screens and lead compounds. We also consider experimental approaches for in vitro drug assays. The Figure illustrates the current Chagas disease drug-discovery and development landscape.

  9. A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways

    Directory of Open Access Journals (Sweden)

    Scott A. Ochsner

    2017-03-01

    Full Text Available Public transcriptomic assets in the nuclear receptor (NR signaling field hold considerable collective potential for exposing underappreciated aspects of NR regulation of gene expression. This potential is undermined however by a series of enduring informatic pain points that retard the routine re-use of these datasets. Here we describe a coordinated biocuration and web development approach to redress this situation that is closely aligned with ideals articulated in the FAIR (findable, accessible, interoperable, re-usable principles on data stewardship. To improve findability, biocurators engage authors of studies in collaborating journals to secure datasets for deposition in public archives. Annotated derivatives of the archived datasets are assigned digital object identifiers and regulatory molecule identifiers that support persistent linkages between datasets and their associated research articles, integration in relevant records in gene and small molecule knowledgebases, and indexing by dataset search engines. To enhance their accessibility and interoperability, datasets are visualizable in responsively designed web pages, retrievable in machine-readable spreadsheets, or through an application programming interface. Re-use of the datasets is supported by their interrogation as a universe of data points through the Transcriptomine search engine, highlighting transcriptional intersections between NR signaling pathways, physiological processes and disease states. We illustrate the value of our approach in connecting disparate research communities using a use case of persistent interoperability between the Nuclear Receptor Signaling Atlas and the Pharmacogenomics Knowledgebase. Our FAIR-aligned model demonstrates the enduring value of discovery-scale datasets that accrues from their systematic compilation, biocuration and distribution across the digital biomedical research enterprise.

  10. A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

    Science.gov (United States)

    Kothari, Cartik R; Payne, Philip R O

    2015-01-01

    In this paper, we present a semantic, metadata based knowledge discovery methodology for identifying teams of researchers from diverse backgrounds who can collaborate on interdisciplinary research projects: projects in areas that have been identified as high-impact areas at The Ohio State University. This methodology involves the semantic annotation of keywords and the postulation of semantic metrics to improve the efficiency of the path exploration algorithm as well as to rank the results. Results indicate that our methodology can discover groups of experts from diverse areas who can collaborate on translational research projects.

  11. Novel Technology for Protein-Protein Interaction-based Targeted Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jung Me Hwang

    2011-12-01

    Full Text Available We have developed a simple but highly efficient in-cell protein-protein interaction (PPI discovery system based on the translocation properties of protein kinase C- and its C1a domain in live cells. This system allows the visual detection of trimeric and dimeric protein interactions including cytosolic, nuclear, and/or membrane proteins with their cognate ligands. In addition, this system can be used to identify pharmacological small compounds that inhibit specific PPIs. These properties make this PPI system an attractive tool for screening drug candidates and mapping the protein interactome.

  12. Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

    Directory of Open Access Journals (Sweden)

    Oguzhan Begik

    2017-07-01

    Full Text Available Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA, a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

  13. Repositioning the substrate activity screening (SAS) approach as a fragment-based method for identification of weak binders.

    Science.gov (United States)

    Gladysz, Rafaela; Cleenewerck, Matthias; Joossens, Jurgen; Lambeir, Anne-Marie; Augustyns, Koen; Van der Veken, Pieter

    2014-10-13

    Fragment-based drug discovery (FBDD) has evolved into an established approach for "hit" identification. Typically, most applications of FBDD depend on specialised cost- and time-intensive biophysical techniques. The substrate activity screening (SAS) approach has been proposed as a relatively cheap and straightforward alternative for identification of fragments for enzyme inhibitors. We have investigated SAS for the discovery of inhibitors of oncology target urokinase (uPA). Although our results support the key hypotheses of SAS, we also encountered a number of unreported limitations. In response, we propose an efficient modified methodology: "MSAS" (modified substrate activity screening). MSAS circumvents the limitations of SAS and broadens its scope by providing additional fragments and more coherent SAR data. As well as presenting and validating MSAS, this study expands existing SAR knowledge for the S1 pocket of uPA and reports new reversible and irreversible uPA inhibitor scaffolds. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Proteomic Approaches in Biomarker Discovery: New Perspectives in Cancer Diagnostics

    Science.gov (United States)

    Kocevar, Nina; Komel, Radovan

    2014-01-01

    Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies. PMID:24550697

  15. Anticancer drug discovery and pharmaceutical chemistry: a history.

    Science.gov (United States)

    Braña, Miguel F; Sánchez-Migallón, Ana

    2006-10-01

    There are several procedures for the chemical discovery and design of new drugs from the point of view of the pharmaceutical or medicinal chemistry. They range from classical methods to the very new ones, such as molecular modeling or high throughput screening. In this review, we will consider some historical approaches based on the screening of natural products, the chances for luck, the systematic screening of new chemical entities and serendipity. Another group comprises rational design, as in the case of metabolic pathways, conformation versus configuration and, finally, a brief description on available new targets to be carried out. In each approach, the structure of some examples of clinical interest will be shown.

  16. International Drug Discovery Science and Technology--BIT's Seventh Annual Congress.

    Science.gov (United States)

    Bodovitz, Steven

    2010-01-01

    BIT's Seventh Annual International Drug Discovery Science and Technology Congress, held in Shanghai, included topics covering new therapeutic and technological developments in the field of drug discovery. This conference report highlights selected presentations on open-access approaches to R&D, novel and multifactorial targets, and technologies that assist drug discovery. Investigational drugs discussed include the anticancer agents astuprotimut-r (GlaxoSmithKline plc) and AS-1411 (Antisoma plc).

  17. Performance Evaluation of a Cluster-Based Service Discovery Protocol for Heterogeneous Wireless Sensor Networks

    NARCIS (Netherlands)

    Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

    2006-01-01

    Abstract—This paper evaluates the performance in terms of resource consumption of a service discovery protocol proposed for heterogeneous Wireless Sensor Networks (WSNs). The protocol is based on a clustering structure, which facilitates the construction of a distributed directory. Nodes with higher

  18. When fragments link : a bibliometric perspective on the development of fragment-based drug discovery

    NARCIS (Netherlands)

    Romasanta, A.K.S.; van der Sijde, P.C.; Hellsten, I.; Hubbard, Roderick E.; Keseru, Gyorgy M.; van Muijlwijk-Koezen, Jacqueline E.; de Esch, I.J.P.

    2018-01-01

    Fragment-based drug discovery (FBDD) is a highly interdisciplinary field, rich in ideas integrated from pharmaceutical sciences, chemistry, biology, and physics, among others. To enrich our understanding of the development of the field, we used bibliometric techniques to analyze 3642 publications in

  19. Infrared and Raman Spectroscopy: A Discovery-Based Activity for the General Chemistry Curriculum

    Science.gov (United States)

    Borgsmiller, Karen L.; O'Connell, Dylan J.; Klauenberg, Kathryn M.; Wilson, Peter M.; Stromberg, Christopher J.

    2012-01-01

    A discovery-based method is described for incorporating the concepts of IR and Raman spectroscopy into the general chemistry curriculum. Students use three sets of springs to model the properties of single, double, and triple covalent bonds. Then, Gaussian 03W molecular modeling software is used to illustrate the relationship between bond…

  20. An Inquiry-Based Biochemistry Laboratory Structure Emphasizing Competency in the Scientific Process: A Guided Approach with an Electronic Notebook Format

    Science.gov (United States)

    Hall, Mona L.; Vardar-Ulu, Didem

    2014-01-01

    The laboratory setting is an exciting and gratifying place to teach because you can actively engage the students in the learning process through hands-on activities; it is a dynamic environment amenable to collaborative work, critical thinking, problem-solving and discovery. The guided inquiry-based approach described here guides the students…

  1. Venomics-Accelerated Cone Snail Venom Peptide Discovery

    Science.gov (United States)

    Himaya, S. W. A.

    2018-01-01

    Cone snail venoms are considered a treasure trove of bioactive peptides. Despite over 800 species of cone snails being known, each producing over 1000 venom peptides, only about 150 unique venom peptides are structurally and functionally characterized. To overcome the limitations of the traditional low-throughput bio-discovery approaches, multi-omics systems approaches have been introduced to accelerate venom peptide discovery and characterisation. This “venomic” approach is starting to unravel the full complexity of cone snail venoms and to provide new insights into their biology and evolution. The main challenge for venomics is the effective integration of transcriptomics, proteomics, and pharmacological data and the efficient analysis of big datasets. Novel database search tools and visualisation techniques are now being introduced that facilitate data exploration, with ongoing advances in related omics fields being expected to further enhance venomics studies. Despite these challenges and future opportunities, cone snail venomics has already exponentially expanded the number of novel venom peptide sequences identified from the species investigated, although most novel conotoxins remain to be pharmacologically characterised. Therefore, efficient high-throughput peptide production systems and/or banks of miniaturized discovery assays are required to overcome this bottleneck and thus enhance cone snail venom bioprospecting and accelerate the identification of novel drug leads. PMID:29522462

  2. Venomics-Accelerated Cone Snail Venom Peptide Discovery

    Directory of Open Access Journals (Sweden)

    S. W. A. Himaya

    2018-03-01

    Full Text Available Cone snail venoms are considered a treasure trove of bioactive peptides. Despite over 800 species of cone snails being known, each producing over 1000 venom peptides, only about 150 unique venom peptides are structurally and functionally characterized. To overcome the limitations of the traditional low-throughput bio-discovery approaches, multi-omics systems approaches have been introduced to accelerate venom peptide discovery and characterisation. This “venomic” approach is starting to unravel the full complexity of cone snail venoms and to provide new insights into their biology and evolution. The main challenge for venomics is the effective integration of transcriptomics, proteomics, and pharmacological data and the efficient analysis of big datasets. Novel database search tools and visualisation techniques are now being introduced that facilitate data exploration, with ongoing advances in related omics fields being expected to further enhance venomics studies. Despite these challenges and future opportunities, cone snail venomics has already exponentially expanded the number of novel venom peptide sequences identified from the species investigated, although most novel conotoxins remain to be pharmacologically characterised. Therefore, efficient high-throughput peptide production systems and/or banks of miniaturized discovery assays are required to overcome this bottleneck and thus enhance cone snail venom bioprospecting and accelerate the identification of novel drug leads.

  3. Research on Hotspot Discovery in Internet Public Opinions Based on Improved -Means

    Directory of Open Access Journals (Sweden)

    Gensheng Wang

    2013-01-01

    Full Text Available How to discover hotspot in the Internet public opinions effectively is a hot research field for the researchers related which plays a key role for governments and corporations to find useful information from mass data in the Internet. An improved -means algorithm for hotspot discovery in internet public opinions is presented based on the analysis of existing defects and calculation principle of original -means algorithm. First, some new methods are designed to preprocess website texts, select and express the characteristics of website texts, and define the similarity between two website texts, respectively. Second, clustering principle and the method of initial classification centers selection are analyzed and improved in order to overcome the limitations of original -means algorithm. Finally, the experimental results verify that the improved algorithm can improve the clustering stability and classification accuracy of hotspot discovery in internet public opinions when used in practice.

  4. Computational methods for a three-dimensional model of the petroleum-discovery process

    Science.gov (United States)

    Schuenemeyer, J.H.; Bawiec, W.J.; Drew, L.J.

    1980-01-01

    A discovery-process model devised by Drew, Schuenemeyer, and Root can be used to predict the amount of petroleum to be discovered in a basin from some future level of exploratory effort: the predictions are based on historical drilling and discovery data. Because marginal costs of discovery and production are a function of field size, the model can be used to make estimates of future discoveries within deposit size classes. The modeling approach is a geometric one in which the area searched is a function of the size and shape of the targets being sought. A high correlation is assumed between the surface-projection area of the fields and the volume of petroleum. To predict how much oil remains to be found, the area searched must be computed, and the basin size and discovery efficiency must be estimated. The basin is assumed to be explored randomly rather than by pattern drilling. The model may be used to compute independent estimates of future oil at different depth intervals for a play involving multiple producing horizons. We have written FORTRAN computer programs that are used with Drew, Schuenemeyer, and Root's model to merge the discovery and drilling information and perform the necessary computations to estimate undiscovered petroleum. These program may be modified easily for the estimation of remaining quantities of commodities other than petroleum. ?? 1980.

  5. Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex.

    Science.gov (United States)

    Hao, Ge-Fei; Wang, Fu; Li, Hui; Zhu, Xiao-Lei; Yang, Wen-Chao; Huang, Li-Shar; Wu, Jia-Wei; Berry, Edward A; Yang, Guang-Fu

    2012-07-11

    A critical challenge to the fragment-based drug discovery (FBDD) is its low-throughput nature due to the necessity of biophysical method-based fragment screening. Herein, a method of pharmacophore-linked fragment virtual screening (PFVS) was successfully developed. Its application yielded the first picomolar-range Q(o) site inhibitors of the cytochrome bc(1) complex, an important membrane protein for drug and fungicide discovery. Compared with the original hit compound 4 (K(i) = 881.80 nM, porcine bc(1)), the most potent compound 4f displayed 20 507-fold improved binding affinity (K(i) = 43.00 pM). Compound 4f was proved to be a noncompetitive inhibitor with respect to the substrate cytochrome c, but a competitive inhibitor with respect to the substrate ubiquinol. Additionally, we determined the crystal structure of compound 4e (K(i) = 83.00 pM) bound to the chicken bc(1) at 2.70 Å resolution, providing a molecular basis for understanding its ultrapotency. To our knowledge, this study is the first application of the FBDD method in the discovery of picomolar inhibitors of a membrane protein. This work demonstrates that the novel PFVS approach is a high-throughput drug discovery method, independent of biophysical screening techniques.

  6. Application of lean manufacturing concepts to drug discovery: rapid analogue library synthesis.

    Science.gov (United States)

    Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas

    2006-01-01

    The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.

  7. A history of the DNA repair and mutagenesis field: The discovery of base excision repair.

    Science.gov (United States)

    Friedberg, Errol C

    2016-01-01

    This article reviews the early history of the discovery of an DNA repair pathway designated as base excision repair (BER), since in contrast to the enzyme-catalyzed removal of damaged bases from DNA as nucleotides [called nucleotide excision repair (NER)], BER involves the removal of damaged or inappropriate bases, such as the presence of uracil instead of thymine, from DNA as free bases. Copyright © 2015. Published by Elsevier B.V.

  8. Discovery and annotation of small proteins using genomics, proteomics and computational approaches

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaohan; Tschaplinski, Timothy J.; Hurst, Gregory B.; Jawdy, Sara; Abraham, Paul E.; Lankford, Patricia K.; Adams, Rachel M.; Shah, Manesh B.; Hettich, Robert L.; Lindquist, Erika; Kalluri, Udaya C.; Gunter, Lee E.; Pennacchio, Christa; Tuskan, Gerald A.

    2011-03-02

    Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

  9. Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

    Science.gov (United States)

    Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

    2014-01-01

    Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

  10. Price discovery in a continuous-time setting

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Fernandes, Marcelo; Scherrer, Cristina

    We formulate a continuous-time price discovery model in which the price discovery measure varies (stochastically) at daily frequency. We estimate daily measures of price discovery using a kernel-based OLS estimator instead of running separate daily VECM regressions as standard in the literature. We...... show that our estimator is not only consistent, but also outperforms the standard daily VECM in finite samples. We illustrate our theoretical findings by studying the price discovery process of 10 actively traded stocks in the U.S. from 2007 to 2013....

  11. Rationale for a natural products approach to herbicide discovery.

    Science.gov (United States)

    Dayan, Franck E; Owens, Daniel K; Duke, Stephen O

    2012-04-01

    Weeds continue to evolve resistance to all the known modes of herbicidal action, but no herbicide with a new target site has been commercialized in nearly 20 years. The so-called 'new chemistries' are simply molecules belonging to new chemical classes that have the same mechanisms of action as older herbicides (e.g. the protoporphyrinogen-oxidase-inhibiting pyrimidinedione saflufenacil or the very-long-chain fatty acid elongase targeting sulfonylisoxazoline herbicide pyroxasulfone). Therefore, the number of tools to manage weeds, and in particular those that can control herbicide-resistant weeds, is diminishing rapidly. There is an imminent need for truly innovative classes of herbicides that explore chemical spaces and interact with target sites not previously exploited by older active ingredients. This review proposes a rationale for a natural-products-centered approach to herbicide discovery that capitalizes on the structural diversity and ingenuity afforded by these biologically active compounds. The natural process of extended-throughput screening (high number of compounds tested on many potential target sites over long periods of times) that has shaped the evolution of natural products tends to generate molecules tailored to interact with specific target sites. As this review shows, there is generally little overlap between the mode of action of natural and synthetic phytotoxins, and more emphasis should be placed on applying methods that have proved beneficial to the pharmaceutical industry to solve problems in the agrochemical industry. Published 2012 by John Wiley & Sons, Ltd.

  12. Clinical impact of recent genetic discoveries in osteoporosis

    Directory of Open Access Journals (Sweden)

    Mitchell BD

    2013-10-01

    Full Text Available Braxton D Mitchell, Elizabeth A StreetenDepartment of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, and Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD, USAAbstract: Osteoporotic fracture carries an enormous public health burden in terms of mortality and morbidity. Current approaches to identify individuals at high risk for fracture are based on assessment of bone mineral density and presence of other osteoporosis risk factors. Bone mineral density and susceptibility to osteoporotic fractures are highly heritable, and over 60 loci have been robustly associated with one or both traits through genome-wide association studies carried out over the past 7 years. In this review, we discuss opportunities and challenges for incorporating these genetic discoveries into strategies to prevent osteoporotic fracture and translating new insights obtained from these discoveries into development of new therapeutic targets.Keywords: bone mineral density, genome-wide association studies, osteoporosis, prediction, fracture, genetics

  13. Four disruptive strategies for removing drug discovery bottlenecks.

    Science.gov (United States)

    Ekins, Sean; Waller, Chris L; Bradley, Mary P; Clark, Alex M; Williams, Antony J

    2013-03-01

    Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Discovery of novel dengue virus NS5 methyltransferase non-nucleoside inhibitors by fragment-based drug design.

    Science.gov (United States)

    Benmansour, Fatiha; Trist, Iuni; Coutard, Bruno; Decroly, Etienne; Querat, Gilles; Brancale, Andrea; Barral, Karine

    2017-01-05

    With the aim to help drug discovery against dengue virus (DENV), a fragment-based drug design approach was applied to identify ligands targeting a main component of DENV replication complex: the NS5 AdoMet-dependent mRNA methyltransferase (MTase) domain, playing an essential role in the RNA capping process. Herein, we describe the identification of new inhibitors developed using fragment-based, structure-guided linking and optimization techniques. Thermal-shift assay followed by a fragment-based X-ray crystallographic screening lead to the identification of three fragment hits binding DENV MTase. We considered linking two of them, which bind to proximal sites of the AdoMet binding pocket, in order to improve their potency. X-ray crystallographic structures and computational docking were used to guide the fragment linking, ultimately leading to novel series of non-nucleoside inhibitors of flavivirus MTase, respectively N-phenyl-[(phenylcarbamoyl)amino]benzene-1-sulfonamide and phenyl [(phenylcarbamoyl)amino]benzene-1-sulfonate derivatives, that show a 10-100-fold stronger inhibition of 2'-O-MTase activity compared to the initial fragments. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  15. Discovery in a World of Mashups

    Science.gov (United States)

    King, T. A.; Ritschel, B.; Hourcle, J. A.; Moon, I. S.

    2014-12-01

    When the first digital information was stored electronically, discovery of what existed was through file names and the organization of the file system. With the advent of networks, digital information was shared on a wider scale, but discovery remained based on file and folder names. With a growing number of information sources, named based discovery quickly became ineffective. The keyword based search engine was one of the first types of a mashup in the world of Web 1.0. Embedded links from one document to another with prescribed relationships between files and the world of Web 2.0 was formed. Search engines like Google used the links to improve search results and a worldwide mashup was formed. While a vast improvement, the need for semantic (meaning rich) discovery was clear, especially for the discovery of scientific data. In response, every science discipline defined schemas to describe their type of data. Some core schemas where shared, but most schemas are custom tailored even though they share many common concepts. As with the networking of information sources, science increasingly relies on data from multiple disciplines. So there is a need to bring together multiple sources of semantically rich information. We explore how harvesting, conceptual mapping, facet based search engines, search term promotion, and style sheets can be combined to create the next generation of mashups in the emerging world of Web 3.0. We use NASA's Planetary Data System and NASA's Heliophysics Data Environment to illustrate how to create a multi-discipline mash-up.

  16. Semi-automated knowledge discovery: identifying and profiling human trafficking

    Science.gov (United States)

    Poelmans, Jonas; Elzinga, Paul; Ignatov, Dmitry I.; Kuznetsov, Sergei O.

    2012-11-01

    We propose an iterative and human-centred knowledge discovery methodology based on formal concept analysis. The proposed approach recognizes the important role of the domain expert in mining real-world enterprise applications and makes use of specific domain knowledge, including human intelligence and domain-specific constraints. Our approach was empirically validated at the Amsterdam-Amstelland police to identify suspects and victims of human trafficking in 266,157 suspicious activity reports. Based on guidelines of the Attorney Generals of the Netherlands, we first defined multiple early warning indicators that were used to index the police reports. Using concept lattices, we revealed numerous unknown human trafficking and loverboy suspects. In-depth investigation by the police resulted in a confirmation of their involvement in illegal activities resulting in actual arrestments been made. Our human-centred approach was embedded into operational policing practice and is now successfully used on a daily basis to cope with the vastly growing amount of unstructured information.

  17. Upnp-Based Discovery And Management Of Hypervisors And Virtual Machines

    Directory of Open Access Journals (Sweden)

    Sławomir Zieliński

    2011-01-01

    Full Text Available The paper introduces a Universal Plug and Play based discovery and management toolkitthat facilitates collaboration between cloud infrastructure providers and users. The presentedtools construct a unified hierarchy of devices and their management-related services, thatrepresents the current deployment of users’ (virtual infrastructures in the provider’s (physicalinfrastructure as well as the management interfaces of respective devices. The hierarchycan be used to enhance the capabilities of the provider’s infrastructure management system.To maintain user independence, the set of management operations exposed by a particulardevice is always defined by the device owner (either the provider or user.

  18. Orphan diseases: state of the drug discovery art.

    Science.gov (United States)

    Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P

    2017-06-01

    Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.

  19. Computer-Assisted Discovery and Proof

    Energy Technology Data Exchange (ETDEWEB)

    Bailey, David H.; Borwein, Jonathan M.

    2007-12-10

    With the advent of powerful, widely-available mathematical software, combined with ever-faster computer hardware, we are approaching a day when both the discovery and proof of mathematical facts can be done in a computer-assisted manner. his article presents several specific examples of this new paradigm in action.

  20. Discovery and validation of information theory-based transcription factor and cofactor binding site motifs.

    Science.gov (United States)

    Lu, Ruipeng; Mucaki, Eliseos J; Rogan, Peter K

    2017-03-17

    Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, discovered 23 cofactor motifs for 127 TFs and revealed six high-confidence novel motifs. The reliability and accuracy of these iPWMs were determined via four independent validation methods, including the detection of experimentally proven binding sites, explanation of effects of characterized SNPs, comparison with previously published motifs and statistical analyses. We also predict previously unreported TF coregulatory interactions (e.g. TF complexes). These iPWMs constitute a powerful tool for predicting the effects of sequence variants in known binding sites, performing mutation analysis on regulatory SNPs and predicting previously unrecognized binding sites and target genes. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  1. Deep data: discovery and visualization Application to hyperspectral ALMA imagery

    Science.gov (United States)

    Merényi, Erzsébet; Taylor, Joshua; Isella, Andrea

    2017-06-01

    Leading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name ``NeuroScope'' are efficient for ``Big Data'' due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.

  2. Integrated analytical approaches towards toxic algal natural products discovery

    DEFF Research Database (Denmark)

    Larsen, Thomas Ostenfeld; Rasmussen, Silas Anselm; Gedsted Andersen, Mikael

    Microalgae are known to produce toxins which affect the marine ecosystems. This include compounds active against competitors, grazers and in many cases also fish (1,2). Many strategies can be followed for discovery of novel bioactive secondary metabolites from marine sources. We have previously...... is dereplication, where we use explorative solid-phase extraction (E-SPE), and UHPLC-state-of-the-art high resolution mass spectrometry (waste time isolating and elucidating...

  3. The Discovery of Insulin: A Case Study of Scientific Methodology

    Science.gov (United States)

    Stansfield, William D.

    2012-01-01

    The nature of scientific research sometimes involves a trial-and-error procedure. Popular reviews of successful results from this approach often sanitize the story by omitting unsuccessful trials, thus painting the rosy impression that research simply follows a direct route from hypothesis to experiment to scientific discovery. The discovery of…

  4. Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox.

    Science.gov (United States)

    Leveridge, Melanie; Chung, Chun-Wa; Gross, Jeffrey W; Phelps, Christopher B; Green, Darren

    2018-06-01

    There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.

  5. A fragment-based approach leading to the discovery of a novel binding site and the selective CK2 inhibitor CAM4066.

    Science.gov (United States)

    De Fusco, Claudia; Brear, Paul; Iegre, Jessica; Georgiou, Kathy Hadje; Sore, Hannah F; Hyvönen, Marko; Spring, David R

    2017-07-01

    Recently we reported the discovery of a potent and selective CK2α inhibitor CAM4066. This compound inhibits CK2 activity by exploiting a pocket located outside the ATP binding site (αD pocket). Here we describe in detail the journey that led to the discovery of CAM4066 using the challenging fragment linking strategy. Specifically, we aimed to develop inhibitors by linking a high-affinity fragment anchored in the αD site to a weakly binding warhead fragment occupying the ATP site. Moreover, we describe the remarkable impact that molecular modelling had on the development of this novel chemical tool. The work described herein shows potential for the development of a novel class of CK2 inhibitors. Copyright © 2017. Published by Elsevier Ltd.

  6. Discovering the Network Topology: An Efficient Approach for SDN

    Directory of Open Access Journals (Sweden)

    Leonardo OCHOA-ADAY

    2016-11-01

    Full Text Available Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS, among many others. Recent technologies like Software-Defined Networks (SDN have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.

  7. Enabling Open Research Data Discovery through a Recommender System

    Science.gov (United States)

    Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic

    2017-04-01

    Government agencies, universities, research and nonprofit organizations are increasingly publishing their datasets to promote transparency, induce new research and generate economic value through the development of new products or services. The datasets may be downloaded from various data portals (data repositories) which are general or domain-specific. The Registry of Research Data Repository (re3data.org) lists more than 2500 such data repositories from around the globe. Data portals allow keyword search and faceted navigation to facilitate discovery of research datasets. However, the volume and variety of datasets have made finding relevant datasets more difficult. Common dataset search mechanisms may be time consuming, may produce irrelevant results and are primarily suitable for users who are familiar with the general structure and contents of the respective database. Therefore, we need new approaches to support research data discovery. Recommender systems offer new possibilities for users to find datasets that are relevant to their research interests. This study presents a recommender system developed for the CSIRO Data Access Portal (DAP, http://data.csiro.au). The datasets hosted on the portal are diverse, published by researchers from 13 business units in the organisation. The goal of the study is not to replace the current search mechanisms on the data portal, but rather to extend the data discovery through an exploratory search, in this case by building a recommender system. We adopted a hybrid recommendation approach, comprising content-based filtering and item-item collaborative filtering. The content-based filtering computes similarities between datasets based on metadata such as title, keywords, descriptions, fields of research, location, contributors, etc. The collaborative filtering utilizes user search behaviour and download patterns derived from the server logs to determine similar datasets. Similarities above are then combined with different

  8. Simulated JWST/NIRISS Transit Spectroscopy of Anticipated TESS Planets Compared to Select Discoveries from Space-Based and Ground-Based Surveys

    Science.gov (United States)

    Louie, Dana; Deming, Drake; Albert, Loic; Bouma, Luke; Bean, Jacob; Lopez-Morales, Mercedes

    2018-01-01

    The Transiting Exoplanet Survey Satellite (TESS) will embark in 2018 on a 2-year wide-field survey mission of most of the celestial sky, discovering over a thousand super-Earth and sub-Neptune-sized exoplanets potentially suitable for follow-up observations using the James Webb Space Telescope (JWST). Bouma et al. (2017) and Sullivan et al. (2015) used Monte Carlo simulations to predict the properties of the planetary systems that TESS is likely to detect, basing their simulations upon Kepler-derived planet occurrence rates and photometric performance models for the TESS cameras. We employed a JWST Near InfraRed Imager and Slitless Spectrograph (NIRISS) simulation tool to estimate the signal-to-noise (S/N) that JWST/NIRISS will attain in transmission spectroscopy of these anticipated TESS discoveries, and we then compared the S/N for anticipated TESS discoveries to our estimates of S/N for 18 known exoplanets. We analyzed the sensitivity of our results to planetary composition, cloud cover, and presence of an observational noise floor. We find that only a few anticipated TESS discoveries in the terrestrial planet regime will result in better JWST/NIRISS S/N than currently known exoplanets, such as the TRAPPIST-1 planets, GJ1132b, or LHS1140b. However, we emphasize that this outcome is based upon Kepler-derived occurrence rates, and that co-planar compact systems (e.g. TRAPPIST-1) were not included in predicting the anticipated TESS planet yield. Furthermore, our results show that several hundred anticipated TESS discoveries in the super-Earth and sub-Neptune regime will produce S/N higher than currently known exoplanets such as K2-3b or K2-3c. We apply our results to estimate the scope of a JWST follow-up observation program devoted to mapping the transition region between high molecular weight and primordial planetary atmospheres.

  9. Volatility Discovery

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Scherrer, Cristina; Papailias, Fotis

    The price discovery literature investigates how homogenous securities traded on different markets incorporate information into prices. We take this literature one step further and investigate how these markets contribute to stochastic volatility (volatility discovery). We formally show...... that the realized measures from homogenous securities share a fractional stochastic trend, which is a combination of the price and volatility discovery measures. Furthermore, we show that volatility discovery is associated with the way that market participants process information arrival (market sensitivity......). Finally, we compute volatility discovery for 30 actively traded stocks in the U.S. and report that Nyse and Arca dominate Nasdaq....

  10. A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

    Science.gov (United States)

    Guerrero, Ginés D.; Imbernón, Baldomero; García, José M.

    2014-01-01

    Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor. PMID:25025055

  11. Novel Small Molecule Inhibitors of Choline Kinase Identified by Fragment-Based Drug Discovery.

    Science.gov (United States)

    Zech, Stephan G; Kohlmann, Anna; Zhou, Tianjun; Li, Feng; Squillace, Rachel M; Parillon, Lois E; Greenfield, Matthew T; Miller, David P; Qi, Jiwei; Thomas, R Mathew; Wang, Yihan; Xu, Yongjin; Miret, Juan J; Shakespeare, William C; Zhu, Xiaotian; Dalgarno, David C

    2016-01-28

    Choline kinase α (ChoKα) is an enzyme involved in the synthesis of phospholipids and thereby plays key roles in regulation of cell proliferation, oncogenic transformation, and human carcinogenesis. Since several inhibitors of ChoKα display antiproliferative activity in both cellular and animal models, this novel oncogene has recently gained interest as a promising small molecule target for cancer therapy. Here we summarize our efforts to further validate ChoKα as an oncogenic target and explore the activity of novel small molecule inhibitors of ChoKα. Starting from weakly binding fragments, we describe a structure based lead discovery approach, which resulted in novel highly potent inhibitors of ChoKα. In cancer cell lines, our lead compounds exhibit a dose-dependent decrease of phosphocholine, inhibition of cell growth, and induction of apoptosis at low micromolar concentrations. The druglike lead series presented here is optimizable for improvements in cellular potency, drug target residence time, and pharmacokinetic parameters. These inhibitors may be utilized not only to further validate ChoKα as antioncogenic target but also as novel chemical matter that may lead to antitumor agents that specifically interfere with cancer cell metabolism.

  12. NEW COMPLETENESS METHODS FOR ESTIMATING EXOPLANET DISCOVERIES BY DIRECT DETECTION

    International Nuclear Information System (INIS)

    Brown, Robert A.; Soummer, Remi

    2010-01-01

    We report on new methods for evaluating realistic observing programs that search stars for planets by direct imaging, where observations are selected from an optimized star list and stars can be observed multiple times. We show how these methods bring critical insight into the design of the mission and its instruments. These methods provide an estimate of the outcome of the observing program: the probability distribution of discoveries (detection and/or characterization) and an estimate of the occurrence rate of planets (η). We show that these parameters can be accurately estimated from a single mission simulation, without the need for a complete Monte Carlo mission simulation, and we prove the accuracy of this new approach. Our methods provide tools to define a mission for a particular science goal; for example, a mission can be defined by the expected number of discoveries and its confidence level. We detail how an optimized star list can be built and how successive observations can be selected. Our approach also provides other critical mission attributes, such as the number of stars expected to be searched and the probability of zero discoveries. Because these attributes depend strongly on the mission scale (telescope diameter, observing capabilities and constraints, mission lifetime, etc.), our methods are directly applicable to the design of such future missions and provide guidance to the mission and instrument design based on scientific performance. We illustrate our new methods with practical calculations and exploratory design reference missions for the James Webb Space Telescope (JWST) operating with a distant starshade to reduce scattered and diffracted starlight on the focal plane. We estimate that five habitable Earth-mass planets would be discovered and characterized with spectroscopy, with a probability of zero discoveries of 0.004, assuming a small fraction of JWST observing time (7%), η = 0.3, and 70 observing visits, limited by starshade fuel.

  13. Drug discovery for male subfertility using high-throughput screening: a new approach to an unsolved problem.

    Science.gov (United States)

    Martins da Silva, Sarah J; Brown, Sean G; Sutton, Keith; King, Louise V; Ruso, Halil; Gray, David W; Wyatt, Paul G; Kelly, Mark C; Barratt, Christopher L R; Hope, Anthony G

    2017-05-01

    Can pharma drug discovery approaches be utilized to transform investigation into novel therapeutics for male infertility? High-throughput screening (HTS) is a viable approach to much-needed drug discovery for male factor infertility. There is both huge demand and a genuine clinical need for new treatment options for infertile men. However, the time, effort and resources required for drug discovery are currently exorbitant, due to the unique challenges of the cellular, physical and functional properties of human spermatozoa and a lack of appropriate assay platform. Spermatozoa were obtained from healthy volunteer research donors and subfertile patients undergoing IVF/ICSI at a hospital-assisted reproductive techniques clinic between January 2012 and November 2016. A HTS assay was developed and validated using intracellular calcium ([Ca2+]i) as a surrogate for motility in human spermatozoa. Calcium fluorescence was detected using a Flexstation microplate reader (384-well platform) and compared with responses evoked by progesterone, a compound known to modify a number of biologically relevant behaviours in human spermatozoa. Hit compounds identified following single point drug screen (10 μM) of an ion channel-focussed library assembled by the University of Dundee Drug Discovery Unit were rescreened to ensure potency using standard 10 point half-logarithm concentration curves, and tested for purity and integrity using liquid chromatography and mass spectrometry. Hit compounds were grouped by structure activity relationships and five representative compounds then further investigated for direct effects on spermatozoa, using computer-assisted sperm assessment, sperm penetration assay and whole-cell patch clamping. Of the 3242 ion channel library ligands screened, 384 compounds (11.8%) elicited a statistically significant increase in calcium fluorescence, with greater than 3× median absolute deviation above the baseline. Seventy-four compounds eliciting ≥50% increase

  14. NetiNeti: discovery of scientific names from text using machine learning methods

    Directory of Open Access Journals (Sweden)

    Akella Lakshmi

    2012-08-01

    Full Text Available Abstract Background A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. Results We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing, a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE. NetiNeti performs better (precision = 98.9% and recall = 70.5% compared to a popular dictionary based approach (precision = 97.5% and recall = 54.3% on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central’s full text articles annotated with scientific names, the precision and recall values are 98.5% and 96.2% respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Conclusions We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.

  15. State of the Art in Tumor Antigen and Biomarker Discovery

    International Nuclear Information System (INIS)

    Even-Desrumeaux, Klervi; Baty, Daniel; Chames, Patrick

    2011-01-01

    Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology

  16. Immunophenotype Discovery, Hierarchical Organization, and Template-based Classification of Flow Cytometry Samples

    Directory of Open Access Journals (Sweden)

    Ariful Azad

    2016-08-01

    Full Text Available We describe algorithms for discovering immunophenotypes from large collections of flow cytometry (FC samples, and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters, a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples, while ignoring noise and small sample-specific variations.We have applied the template-base scheme to analyze several data setsincluding one representing a healthy immune system, and one of Acute Myeloid Leukemia (AMLsamples. The last task is challenging due to the phenotypic heterogeneity of the severalsubtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML, and were able to distinguish Acute Promyelocytic Leukemia from other subtypes of AML.

  17. Synthetic biology for pharmaceutical drug discovery

    Directory of Open Access Journals (Sweden)

    Trosset JY

    2015-12-01

    Full Text Available Jean-Yves Trosset,1 Pablo Carbonell2,3 1Bioinformation Research Laboratory, Sup’Biotech, Villejuif, France; 2Faculty of Life Sciences, SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK; 3Department of Experimental and Health Sciences (DCEXS, Research Programme on Biomedical Informatics (GRIB, Hospital del Mar Medical Research Institute (IMIM, Universitat Pompeu Fabra (UPF, Barcelona, Spain Abstract: Synthetic biology (SB is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell–cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity. Keywords: metabolic engineering, plant synthetic biology, natural products, synthetic quorum sensing, drug resistance

  18. Literature-related discovery techniques applied to ocular disease : a vitreous restoration example

    NARCIS (Netherlands)

    Kostoff, Ronald N.; Los, Leonoor I.

    2013-01-01

    Purpose of reviewLiterature-related discovery and innovation (LRDI) is a text mining approach for bridging unconnected disciplines to hypothesize radical discovery. Application to medical problems involves identifying key disease symptoms, and identifying causes and treatments for those symptoms

  19. Research on hotspot discovery in internet public opinions based on improved K-means.

    Science.gov (United States)

    Wang, Gensheng

    2013-01-01

    How to discover hotspot in the Internet public opinions effectively is a hot research field for the researchers related which plays a key role for governments and corporations to find useful information from mass data in the Internet. An improved K-means algorithm for hotspot discovery in internet public opinions is presented based on the analysis of existing defects and calculation principle of original K-means algorithm. First, some new methods are designed to preprocess website texts, select and express the characteristics of website texts, and define the similarity between two website texts, respectively. Second, clustering principle and the method of initial classification centers selection are analyzed and improved in order to overcome the limitations of original K-means algorithm. Finally, the experimental results verify that the improved algorithm can improve the clustering stability and classification accuracy of hotspot discovery in internet public opinions when used in practice.

  20. Research on Hotspot Discovery in Internet Public Opinions Based on Improved K-Means

    Science.gov (United States)

    2013-01-01

    How to discover hotspot in the Internet public opinions effectively is a hot research field for the researchers related which plays a key role for governments and corporations to find useful information from mass data in the Internet. An improved K-means algorithm for hotspot discovery in internet public opinions is presented based on the analysis of existing defects and calculation principle of original K-means algorithm. First, some new methods are designed to preprocess website texts, select and express the characteristics of website texts, and define the similarity between two website texts, respectively. Second, clustering principle and the method of initial classification centers selection are analyzed and improved in order to overcome the limitations of original K-means algorithm. Finally, the experimental results verify that the improved algorithm can improve the clustering stability and classification accuracy of hotspot discovery in internet public opinions when used in practice. PMID:24106496

  1. A contig-based strategy for the genome-wide discovery of microRNAs without complete genome resources.

    Directory of Open Access Journals (Sweden)

    Jun-Zhi Wen

    Full Text Available MicroRNAs (miRNAs are important regulators of many cellular processes and exist in a wide range of eukaryotes. High-throughput sequencing is a mainstream method of miRNA identification through which it is possible to obtain the complete small RNA profile of an organism. Currently, most approaches to miRNA identification rely on a reference genome for the prediction of hairpin structures. However, many species of economic and phylogenetic importance are non-model organisms without complete genome sequences, and this limits miRNA discovery. Here, to overcome this limitation, we have developed a contig-based miRNA identification strategy. We applied this method to a triploid species of edible banana (GCTCV-119, Musa spp. AAA group and identified 180 pre-miRNAs and 314 mature miRNAs, which is three times more than those were predicted by the available dataset-based methods (represented by EST+GSS. Based on the recently published miRNA data set of Musa acuminate, the recall rate and precision of our strategy are estimated to be 70.6% and 92.2%, respectively, significantly better than those of EST+GSS-based strategy (10.2% and 50.0%, respectively. Our novel, efficient and cost-effective strategy facilitates the study of the functional and evolutionary role of miRNAs, as well as miRNA-based molecular breeding, in non-model species of economic or evolutionary interest.

  2. Discovery of accessible locations using region-based geo-social data

    KAUST Repository

    Wang, Yan

    2018-03-17

    Geo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query regions, the top-k accessible location discovery query (k ALDQ) finds k locations that have the highest spatial-density correlations to Q. Both the spatial distances between locations and regions and the POI (point of interest) density within the regions are taken into account. We believe that this type of k ALDQ query can bring significant benefit to many applications such as travel planning, facility allocation, and urban planning. Three challenges exist in k ALDQ: (1) how to model the spatial-density correlation practically, (2) how to prune the search space effectively, and (3) how to schedule the searches from multiple query regions. To tackle the challenges and process k ALDQ effectively and efficiently, we first define a series of spatial and density metrics to model the spatial-density correlation. Then we propose a novel three-phase solution with a pair of upper and lower bounds of the spatial-density correlation and a heuristic scheduling strategy to schedule multiple query regions. Finally, we conduct extensive experiments on real and synthetic spatial data to demonstrate the performance of the developed solutions.

  3. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

    Directory of Open Access Journals (Sweden)

    Nicholas Ekow Thomford

    2018-05-01

    Full Text Available The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of “active compound” has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of ‘organ-on chip’ and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug

  4. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

    Science.gov (United States)

    Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin

    2018-05-25

    The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review

  5. Knowledge Discovery and Data Mining in Iran's Climatic Researches

    Science.gov (United States)

    Karimi, Mostafa

    2013-04-01

    Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for

  6. Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders.

    Science.gov (United States)

    Han, Chanshuai; Chaineau, Mathilde; Chen, Carol X-Q; Beitel, Lenore K; Durcan, Thomas M

    2018-01-01

    Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs) from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop "first-of-their-kind" disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI) and its partners are piloting an "Open Science" model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.

  7. Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders

    Directory of Open Access Journals (Sweden)

    Chanshuai Han

    2018-02-01

    Full Text Available Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop “first-of-their-kind” disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI and its partners are piloting an “Open Science” model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.

  8. Clustering-based approaches to SAGE data mining

    Directory of Open Access Journals (Sweden)

    Wang Haiying

    2008-07-01

    Full Text Available Abstract Serial analysis of gene expression (SAGE is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.

  9. Lead Discovery Strategies for Identification of Chlamydia pneumoniae Inhibitors

    Directory of Open Access Journals (Sweden)

    Leena Hanski

    2016-11-01

    Full Text Available Throughout its known history, the gram-negative bacterium Chlamydia pneumoniae has remained a challenging target for antibacterial chemotherapy and drug discovery. Owing to its well-known propensity for persistence and recent reports on antimicrobial resistence within closely related species, new approaches for targeting this ubiquitous human pathogen are urgently needed. In this review, we describe the strategies that have been successfully applied for the identification of nonconventional antichlamydial agents, including target-based and ligand-based virtual screening, ethnopharmacological approach and pharmacophore-based design of antimicrobial peptide-mimicking compounds. Among the antichlamydial agents identified via these strategies, most translational work has been carried out with plant phenolics. Thus, currently available data on their properties as antichlamydial agents are described, highlighting their potential mechanisms of action. In this context, the role of mitogen-activated protein kinase activation in the intracellular growth and survival of C. pneumoniae is discussed. Owing to the complex and often complementary pathways applied by C. pneumoniae in the different stages of its life cycle, multitargeted therapy approaches are expected to provide better tools for antichlamydial therapy than agents with a single molecular target.

  10. Lead Discovery Strategies for Identification of Chlamydia pneumoniae Inhibitors.

    Science.gov (United States)

    Hanski, Leena; Vuorela, Pia

    2016-11-28

    Throughout its known history, the gram-negative bacterium Chlamydia pneumoniae has remained a challenging target for antibacterial chemotherapy and drug discovery. Owing to its well-known propensity for persistence and recent reports on antimicrobial resistence within closely related species, new approaches for targeting this ubiquitous human pathogen are urgently needed. In this review, we describe the strategies that have been successfully applied for the identification of nonconventional antichlamydial agents, including target-based and ligand-based virtual screening, ethnopharmacological approach and pharmacophore-based design of antimicrobial peptide-mimicking compounds. Among the antichlamydial agents identified via these strategies, most translational work has been carried out with plant phenolics. Thus, currently available data on their properties as antichlamydial agents are described, highlighting their potential mechanisms of action. In this context, the role of mitogen-activated protein kinase activation in the intracellular growth and survival of C . pneumoniae is discussed. Owing to the complex and often complementary pathways applied by C. pneumoniae in the different stages of its life cycle, multitargeted therapy approaches are expected to provide better tools for antichlamydial therapy than agents with a single molecular target.

  11. Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

    Science.gov (United States)

    Ekins, Sean; Madrid, Peter B; Sarker, Malabika; Li, Shao-Gang; Mittal, Nisha; Kumar, Pradeep; Wang, Xin; Stratton, Thomas P; Zimmerman, Matthew; Talcott, Carolyn; Bourbon, Pauline; Travers, Mike; Yadav, Maneesh; Freundlich, Joel S

    2015-01-01

    Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

  12. A Software Engineering Approach based on WebML and BPMN to the Mediation Scenario of the SWS Challenge

    Science.gov (United States)

    Brambilla, Marco; Ceri, Stefano; Valle, Emanuele Della; Facca, Federico M.; Tziviskou, Christina

    Although Semantic Web Services are expected to produce a revolution in the development of Web-based systems, very few enterprise-wide design experiences are available; one of the main reasons is the lack of sound Software Engineering methods and tools for the deployment of Semantic Web applications. In this chapter, we present an approach to software development for the Semantic Web based on classical Software Engineering methods (i.e., formal business process development, computer-aided and component-based software design, and automatic code generation) and on semantic methods and tools (i.e., ontology engineering, semantic service annotation and discovery).

  13. A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

    Directory of Open Access Journals (Sweden)

    Ginés D. Guerrero

    2014-01-01

    Full Text Available Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO. This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.

  14. Fragment-based discovery of potent inhibitors of the anti-apoptotic MCL-1 protein.

    Science.gov (United States)

    Petros, Andrew M; Swann, Steven L; Song, Danying; Swinger, Kerren; Park, Chang; Zhang, Haichao; Wendt, Michael D; Kunzer, Aaron R; Souers, Andrew J; Sun, Chaohong

    2014-03-15

    Apoptosis is regulated by the BCL-2 family of proteins, which is comprised of both pro-death and pro-survival members. Evasion of apoptosis is a hallmark of malignant cells. One way in which cancer cells achieve this evasion is thru overexpression of the pro-survival members of the BCL-2 family. Overexpression of MCL-1, a pro-survival protein, has been shown to be a resistance factor for Navitoclax, a potent inhibitor of BCL-2 and BCL-XL. Here we describe the use of fragment screening methods and structural biology to drive the discovery of novel MCL-1 inhibitors from two distinct structural classes. Specifically, cores derived from a biphenyl sulfonamide and salicylic acid were uncovered in an NMR-based fragment screen and elaborated using high throughput analog synthesis. This culminated in the discovery of selective and potent inhibitors of MCL-1 that may serve as promising leads for medicinal chemistry optimization efforts. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Computational Discovery of Materials Using the Firefly Algorithm

    Science.gov (United States)

    Avendaño-Franco, Guillermo; Romero, Aldo

    Our current ability to model physical phenomena accurately, the increase computational power and better algorithms are the driving forces behind the computational discovery and design of novel materials, allowing for virtual characterization before their realization in the laboratory. We present the implementation of a novel firefly algorithm, a population-based algorithm for global optimization for searching the structure/composition space. This novel computation-intensive approach naturally take advantage of concurrency, targeted exploration and still keeping enough diversity. We apply the new method in both periodic and non-periodic structures and we present the implementation challenges and solutions to improve efficiency. The implementation makes use of computational materials databases and network analysis to optimize the search and get insights about the geometric structure of local minima on the energy landscape. The method has been implemented in our software PyChemia, an open-source package for materials discovery. We acknowledge the support of DMREF-NSF 1434897 and the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research under Contract 54075-ND10.

  16. Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

    Science.gov (United States)

    Fox, P.

    2012-04-01

    The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

  17. Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery.

    Science.gov (United States)

    Williams, Glyn; Ferenczy, György G; Ulander, Johan; Keserű, György M

    2017-04-01

    Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. A SURVEY ON OPTIMIZATION APPROACHES TO SEMANTIC SERVICE DISCOVERY TOWARDS AN INTEGRATED SOLUTION

    Directory of Open Access Journals (Sweden)

    Chellammal Surianarayanan

    2012-07-01

    Full Text Available The process of semantic service discovery using an ontology reasoner such as Pellet is time consuming. This restricts the usage of web services in real time applications having dynamic composition requirements. As performance of semantic service discovery is crucial in service composition, it should be optimized. Various optimization methods are being proposed to improve the performance of semantic discovery. In this work, we investigate the existing optimization methods and broadly classify optimization mechanisms into two categories, namely optimization by efficient reasoning and optimization by efficient matching. Optimization by efficient matching is further classified into subcategories such as optimization by clustering, optimization by inverted indexing, optimization by caching, optimization by hybrid methods, optimization by efficient data structures and optimization by efficient matching algorithms. With a detailed study of different methods, an integrated optimization infrastructure along with matching method has been proposed to improve the performance of semantic matching component. To achieve better optimization the proposed method integrates the effects of caching, clustering and indexing. Theoretical aspects of performance evaluation of the proposed method are discussed.

  19. On reliable discovery of molecular signatures

    Directory of Open Access Journals (Sweden)

    Björkegren Johan

    2009-01-01

    Full Text Available Abstract Background Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability. Results We demonstrate that with modern prediction methods, signatures that yield accurate predictions may still have a high FDR. Further, we show that even signatures with low FDR may fail to replicate in independent studies due to limited statistical power. Thus, neither stability nor predictive accuracy are relevant when FDR control is the primary goal. We therefore develop a general statistical hypothesis testing framework that for the first time provides FDR control for signature discovery. Our method is demonstrated to be correct in simulation studies. When applied to five cancer data sets, the method was able to discover molecular signatures with 5% FDR in three cases, while two data sets yielded no significant findings. Conclusion Our approach enables reliable discovery of molecular signatures from genome-wide data with current sample sizes. The statistical framework developed herein is potentially applicable to a wide range of prediction problems in bioinformatics.

  20. Providing Fast Discovery in D2D Communication with Full Duplex Technology

    DEFF Research Database (Denmark)

    Gatnau, Marta; Berardinelli, Gilberto; Mahmood, Nurul Huda

    2016-01-01

    technology to provide D2D fast discovery. Such framework provides an algorithm to estimate the number of neighbor devices and to dynamically decide the transmission probability, for adapting to network changes and meeting the 10 milliseconds target. Finally, a signaling scheme is proposed to reduce......In Direct Device-to-Device (D2D), the device awareness procedure known as the discovery phase is required prior to the exchange of data. This work considers autonomous devices where the infrastructure is not involved in the discovery procedure. Commonly, the transmission of the discovery message...... the network interference. Results show that our framework performs better than a static approach, reducing the time it takes to complete the discovery phase. In addition, supporting full duplex allows to further reduce the discovery time compared to half duplex transmission mode....

  1. Semantic based cluster content discovery in description first clustering algorithm

    International Nuclear Information System (INIS)

    Khan, M.W.; Asif, H.M.S.

    2017-01-01

    In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase. (author)

  2. BLSSpeller: exhaustive comparative discovery of conserved cis-regulatory elements.

    Science.gov (United States)

    De Witte, Dieter; Van de Velde, Jan; Decap, Dries; Van Bel, Michiel; Audenaert, Pieter; Demeester, Piet; Dhoedt, Bart; Vandepoele, Klaas; Fostier, Jan

    2015-12-01

    The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected. We present a novel algorithm that supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in O.sativa and Zea mays. Furthermore, the method is shown to recover experimentally profiled ga2ox1-like KN1 binding sites in Z.mays. BLSSpeller was written in Java. Source code and manual are available at http://bioinformatics.intec.ugent.be/blsspeller Klaas.Vandepoele@psb.vib-ugent.be or jan.fostier@intec.ugent.be. Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  3. Neptune's Discovery: Le Verrier, Adams, and the Assignment of Credit

    Science.gov (United States)

    Sheehan, William

    2011-01-01

    As one of the most significant achievements of 19th century astronomy, the discovery of Neptune has been the subject of a vast literature. A large part of this literature--beginning with the period immediately after the optical discovery in Berlin--has been the obsession with assigning credit to the two men who attempted to calculate the planet's position (and initially this played out against the international rivalry between France and England). Le Verrier and Adams occupied much different positions in the Scientific Establishments of their respective countries; had markedly different personalities; and approached the investigation using different methods. A psychiatrist and historian of astronomy tries to provide some new contexts to the familiar story of the discovery of Neptune, and argues that the personalities of these two men played crucial roles in their approaches to the problem they set themselves and the way others reacted to their stimuli. Adams had features of high-functioning autism, while Le Verrier's domineering, obsessive, orderly personality--though it allowed him to be immensely productive--eventually led to serious difficulties with his peers (and an outright revolt). Though it took extraordinary smarts to calculate the position of Neptune, the discovery required social skills that these men lacked--and thus the process to discovery was more bumbling and adventitious than it might have been. The discovery of Neptune occurred at a moment when astronomy was changing from that of heroic individuals to team collaborations involving multiple experts, and remains an object lesson in the sociological aspects of scientific endeavor.

  4. Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery

    International Nuclear Information System (INIS)

    Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel

    2009-01-01

    The reliability of ad-hoc networks is gaining popularity in two areas: as a topic of academic interest and as a key performance parameter for defense systems employing this type of network. The ad-hoc network is dynamic and scalable and these descriptions are what attract its users. However, these descriptions are also synonymous for undefined and unpredictable when considering the impacts to the reliability of the system. The configuration of an ad-hoc network changes continuously and this fact implies that no single mathematical expression or graphical depiction can describe the system reliability-wise. Previous research has used mobility and stochastic models to address this challenge successfully. In this paper, the authors leverage the stochastic approach and build upon it a probabilistic solution discovery (PSD) algorithm to optimize the topology for a cluster-based mobile ad-hoc wireless network (MAWN). Specifically, the membership of nodes within the back-bone network or networks will be assigned in such as way as to maximize reliability subject to a constraint on cost. The constraint may also be considered as a non-monetary cost, such as weight, volume, power, or the like. When a cost is assigned to each component, a maximum cost threshold is assigned to the network, and the method is run; the result is an optimized allocation of the radios enabling back-bone network(s) to provide the most reliable network possible without exceeding the allowable cost. The method is intended for use directly as part of the architectural design process of a cluster-based MAWN to efficiently determine an optimal or near-optimal design solution. It is capable of optimizing the topology based upon all-terminal reliability (ATR), all-operating terminal reliability (AoTR), or two-terminal reliability (2TR)

  5. Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

    Science.gov (United States)

    Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

    2016-08-18

    A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed

  6. Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

    Science.gov (United States)

    Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

    2017-02-01

    Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.

  7. Resource-estimation models and predicted discovery

    International Nuclear Information System (INIS)

    Hill, G.W.

    1982-01-01

    Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)

  8. History of the discovery of uranium fission by neutrons

    International Nuclear Information System (INIS)

    Simane, C.

    1989-01-01

    The history is briefly described of the discovery of uranium fission by neutrons, based on the texts of original scientific studies, memories or biographies of those who participated in the discovery and of their contemporaries. Obstacles that stood in the way of the discovery are discussed. It is stated that only a few scientists contributed to the discovery of uranium fission. The fission process itself still remains subject of physical research which studies its detailed laws. (Z.M.). 2 tabs., 16 refs

  9. New perspectives on innovative drug discovery: an overview.

    Science.gov (United States)

    Pan, Si Yuan; Pan, Shan; Yu, Zhi-Ling; Ma, Dik-Lung; Chen, Si-Bao; Fong, Wang-Fun; Han, Yi-Fan; Ko, Kam-Ming

    2010-01-01

    Despite advances in technology, drug discovery is still a lengthy, expensive, difficult, and inefficient process, with a low rate of success. Today, advances in biomedical science have brought about great strides in therapeutic interventions for a wide spectrum of diseases. The advent of biochemical techniques and cutting-edge bio/chemical technologies has made available a plethora of practical approaches to drug screening and design. In 2010, the total sales of the global pharmaceutical market will reach 600 billion US dollars and expand to over 975 billion dollars by 2013. The aim of this review is to summarize available information on contemporary approaches and strategies in the discovery of novel therapeutic agents, especially from the complementary and alternative medicines, including natural products and traditional remedies such as Chinese herbal medicine.

  10. A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

    Directory of Open Access Journals (Sweden)

    Yongyao Jiang

    2018-02-01

    Full Text Available Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1 the system enables semantic query expansion and suggestion to assist users in finding more relevant data; (2 machine-learned ranking is utilized to provide the optimal search ranking based on a number of identified ranking features that can reflect users’ search preferences; (3 a hybrid recommendation module is designed to allow users to discover related data considering metadata attributes and user behavior; (4 an integrated graphic user interface design is developed to quickly and intuitively guide data consumers to the appropriate data resources. As a proof of concept, we focus on a well-defined domain-oceanography and use oceanographic data discovery as an example. Experiments and a search example show that the proposed system can improve the scientific community’s data search experience by providing query expansion, suggestion, better search ranking, and data recommendation via a user-friendly interface.

  11. The limits of de novo DNA motif discovery.

    Directory of Open Access Journals (Sweden)

    David Simcha

    Full Text Available A major challenge in molecular biology is reverse-engineering the cis-regulatory logic that plays a major role in the control of gene expression. This program includes searching through DNA sequences to identify "motifs" that serve as the binding sites for transcription factors or, more generally, are predictive of gene expression across cellular conditions. Several approaches have been proposed for de novo motif discovery-searching sequences without prior knowledge of binding sites or nucleotide patterns. However, unbiased validation is not straightforward. We consider two approaches to unbiased validation of discovered motifs: testing the statistical significance of a motif using a DNA "background" sequence model to represent the null hypothesis and measuring performance in predicting membership in gene clusters. We demonstrate that the background models typically used are "too null," resulting in overly optimistic assessments of significance, and argue that performance in predicting TF binding or expression patterns from DNA motifs should be assessed by held-out data, as in predictive learning. Applying this criterion to common motif discovery methods resulted in universally poor performance, although there is a marked improvement when motifs are statistically significant against real background sequences. Moreover, on synthetic data where "ground truth" is known, discriminative performance of all algorithms is far below the theoretical upper bound, with pronounced "over-fitting" in training. A key conclusion from this work is that the failure of de novo discovery approaches to accurately identify motifs is basically due to statistical intractability resulting from the fixed size of co-regulated gene clusters, and thus such failures do not necessarily provide evidence that unfound motifs are not active biologically. Consequently, the use of prior knowledge to enhance motif discovery is not just advantageous but necessary. An implementation of

  12. Paths of Discovery: Comparing the Search Effectiveness of EBSCO Discovery Service, Summon, Google Scholar, and Conventional Library Resources

    Science.gov (United States)

    Asher, Andrew D.; Duke, Lynda M.; Wilson, Suzanne

    2013-01-01

    In 2011, researchers at Bucknell University and Illinois Wesleyan University compared the search efficacy of Serial Solutions Summon, EBSCO Discovery Service, Google Scholar, and conventional library databases. Using a mixed-methods approach, qualitative and quantitative data were gathered on students' usage of these tools. Regardless of the…

  13. A GIS-based Quantitative Approach for the Search of Clandestine Graves, Italy.

    Science.gov (United States)

    Somma, Roberta; Cascio, Maria; Silvestro, Massimiliano; Torre, Eliana

    2018-05-01

    Previous research on the RAG color-coded prioritization systems for the discovery of clandestine graves has not considered all the factors influencing the burial site choice within a GIS project. The goal of this technical note was to discuss a GIS-based quantitative approach for the search of clandestine graves. The method is based on cross-referenced RAG maps with cumulative suitability factors to host a burial, leading to the editing of different search scenarios for ground searches showing high-(Red), medium-(Amber), and low-(Green) priority areas. The application of this procedure allowed several outcomes to be determined: If the concealment occurs at night, then the "search scenario without the visibility" will be the most effective one; if the concealment occurs in daylight, then the "search scenario with the DSM-based visibility" will be most appropriate; the different search scenarios may be cross-referenced with offender's confessions and eyewitnesses' testimonies to verify the veracity of their statements. © 2017 American Academy of Forensic Sciences.

  14. West Nile Virus Drug Discovery

    Directory of Open Access Journals (Sweden)

    Siew Pheng Lim

    2013-12-01

    Full Text Available The outbreak of West Nile virus (WNV in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development.

  15. Bayesian Modeling of Temporal Coherence in Videos for Entity Discovery and Summarization.

    Science.gov (United States)

    Mitra, Adway; Biswas, Soma; Bhattacharyya, Chiranjib

    2017-03-01

    A video is understood by users in terms of entities present in it. Entity Discovery is the task of building appearance model for each entity (e.g., a person), and finding all its occurrences in the video. We represent a video as a sequence of tracklets, each spanning 10-20 frames, and associated with one entity. We pose Entity Discovery as tracklet clustering, and approach it by leveraging Temporal Coherence (TC): the property that temporally neighboring tracklets are likely to be associated with the same entity. Our major contributions are the first Bayesian nonparametric models for TC at tracklet-level. We extend Chinese Restaurant Process (CRP) to TC-CRP, and further to Temporally Coherent Chinese Restaurant Franchise (TC-CRF) to jointly model entities and temporal segments using mixture components and sparse distributions. For discovering persons in TV serial videos without meta-data like scripts, these methods show considerable improvement over state-of-the-art approaches to tracklet clustering in terms of clustering accuracy, cluster purity and entity coverage. The proposed methods can perform online tracklet clustering on streaming videos unlike existing approaches, and can automatically reject false tracklets. Finally we discuss entity-driven video summarization- where temporal segments of the video are selected based on the discovered entities, to create a semantically meaningful summary.

  16. The impact of genetics on future drug discovery in schizophrenia.

    Science.gov (United States)

    Matsumoto, Mitsuyuki; Walton, Noah M; Yamada, Hiroshi; Kondo, Yuji; Marek, Gerard J; Tajinda, Katsunori

    2017-07-01

    Failures of investigational new drugs (INDs) for schizophrenia have left huge unmet medical needs for patients. Given the recent lackluster results, it is imperative that new drug discovery approaches (and resultant drug candidates) target pathophysiological alterations that are shared in specific, stratified patient populations that are selected based on pre-identified biological signatures. One path to implementing this paradigm is achievable by leveraging recent advances in genetic information and technologies. Genome-wide exome sequencing and meta-analysis of single nucleotide polymorphism (SNP)-based association studies have already revealed rare deleterious variants and SNPs in patient populations. Areas covered: Herein, the authors review the impact that genetics have on the future of schizophrenia drug discovery. The high polygenicity of schizophrenia strongly indicates that this disease is biologically heterogeneous so the identification of unique subgroups (by patient stratification) is becoming increasingly necessary for future investigational new drugs. Expert opinion: The authors propose a pathophysiology-based stratification of genetically-defined subgroups that share deficits in particular biological pathways. Existing tools, including lower-cost genomic sequencing and advanced gene-editing technology render this strategy ever more feasible. Genetically complex psychiatric disorders such as schizophrenia may also benefit from synergistic research with simpler monogenic disorders that share perturbations in similar biological pathways.

  17. Discovery of Cationic Polymers for Non-viral Gene Delivery using Combinatorial Approaches

    Science.gov (United States)

    Barua, Sutapa; Ramos, James; Potta, Thrimoorthy; Taylor, David; Huang, Huang-Chiao; Montanez, Gabriela; Rege, Kaushal

    2015-01-01

    Gene therapy is an attractive treatment option for diseases of genetic origin, including several cancers and cardiovascular diseases. While viruses are effective vectors for delivering exogenous genes to cells, concerns related to insertional mutagenesis, immunogenicity, lack of tropism, decay and high production costs necessitate the discovery of non-viral methods. Significant efforts have been focused on cationic polymers as non-viral alternatives for gene delivery. Recent studies have employed combinatorial syntheses and parallel screening methods for enhancing the efficacy of gene delivery, biocompatibility of the delivery vehicle, and overcoming cellular level barriers as they relate to polymer-mediated transgene uptake, transport, transcription, and expression. This review summarizes and discusses recent advances in combinatorial syntheses and parallel screening of cationic polymer libraries for the discovery of efficient and safe gene delivery systems. PMID:21843141

  18. A MODIFIED ROUTE DISCOVERY APPROACH FOR DYNAMIC SOURCE ROUTING (DSR PROTOCOL IN MOBILE AD-HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    Alaa Azmi Allahham

    2017-02-01

    Full Text Available Mobile Ad-hoc networks (MANETs involved in many applications, whether commercial or military because of their characteristics that do not depend on the infrastructure as well as the freedom movement of their elements, but in return has caused this random mobility of the nodes many of the challenges, where the routing is considered one of these challenges. There are many types of routing protocols that operate within MANET networks, which responsible for finding paths between the source and destination nodes with the modernization of these paths which are constantly changing due to the dynamic topology of the network stemming from the constant random movement of the nodes. The DSR (Dynamic Source Routing routing protocol algorithm is one of these routing protocols which consist of two main stages; route discovery and maintenance, where the route discovery algorithm operates based on blind flooding of request messages. blind flooding is considered as the most well known broadcasting mechanism, it is inefficient in terms of communication and resource utilization, which causing increasing the probability of collisions, repeating send several copies of the same message, as well as increasing the delay. Hence, a new mechanism in route discovery stage and in caching the routes in DSR algorithm according to the node's location in the network and the direction of the broadcast is proposed for better performance especially in terms of delay as well as redundant packets rate. The implementation of proposed algorithms showed positive results in terms of delay, overhead, and improve the performance of MANETs in general.

  19. Search strategy has influenced the discovery rate of human viruses.

    Science.gov (United States)

    Rosenberg, Ronald; Johansson, Michael A; Powers, Ann M; Miller, Barry R

    2013-08-20

    A widely held concern is that the pace of infectious disease emergence has been increasing. We have analyzed the rate of discovery of pathogenic viruses, the preeminent source of newly discovered causes of human disease, from 1897 through 2010. The rate was highest during 1950-1969, after which it moderated. This general picture masks two distinct trends: for arthropod-borne viruses, which comprised 39% of pathogenic viruses, the discovery rate peaked at three per year during 1960-1969, but subsequently fell nearly to zero by 1980; however, the rate of discovery of nonarboviruses remained stable at about two per year from 1950 through 2010. The period of highest arbovirus discovery coincided with a comprehensive program supported by The Rockefeller Foundation of isolating viruses from humans, animals, and arthropod vectors at field stations in Latin America, Africa, and India. The productivity of this strategy illustrates the importance of location, approach, long-term commitment, and sponsorship in the discovery of emerging pathogens.

  20. An experimental evaluation of passage-based process discovery

    NARCIS (Netherlands)

    Verbeek, H.M.W.; Aalst, van der W.M.P.; La Rosa, M.; Soffer, P.

    2013-01-01

    In the area of process mining, the ILP Miner is known for the fact that it always returns a Petri net that perfectly fits a given event log. Like for most process discovery algorithms, its complexity is linear in the size of the event log and exponential in the number of event classes (i.e.,

  1. Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species

    Directory of Open Access Journals (Sweden)

    Kristopher J. L. Irizarry

    2016-01-01

    Full Text Available Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management.

  2. Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species.

    Science.gov (United States)

    Irizarry, Kristopher J L; Bryant, Doug; Kalish, Jordan; Eng, Curtis; Schmidt, Peggy L; Barrett, Gini; Barr, Margaret C

    2016-01-01

    Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management.

  3. Beyond information retrieval: information discovery and multimedia information retrieval

    OpenAIRE

    Roberto Raieli

    2017-01-01

    The paper compares the current methodologies for search and discovery of information and information resources: terminological search and term-based language, own of information retrieval (IR); semantic search and information discovery, being developed mainly through the language of linked data; semiotic search and content-based language, experienced by multimedia information retrieval (MIR).MIR semiotic methodology is, then, detailed.

  4. "Drug" Discovery with the Help of Organic Chemistry.

    Science.gov (United States)

    Itoh, Yukihiro; Suzuki, Takayoshi

    2017-01-01

    The first step in "drug" discovery is to find compounds binding to a potential drug target. In modern medicinal chemistry, the screening of a chemical library, structure-based drug design, and ligand-based drug design, or a combination of these methods, are generally used for identifying the desired compounds. However, they do not necessarily lead to success and there is no infallible method for drug discovery. Therefore, it is important to explore medicinal chemistry based on not only the conventional methods but also new ideas. So far, we have found various compounds as drug candidates. In these studies, some strategies based on organic chemistry have allowed us to find drug candidates, through 1) construction of a focused library using organic reactions and 2) rational design of enzyme inhibitors based on chemical reactions catalyzed by the target enzyme. Medicinal chemistry based on organic chemical reactions could be expected to supplement the conventional methods. In this review, we present drug discovery with the help of organic chemistry showing examples of our explorative studies on histone deacetylase inhibitors and lysine-specific demethylase 1 inhibitors.

  5. Discovery of novel bacterial toxins by genomics and computational biology.

    Science.gov (United States)

    Doxey, Andrew C; Mansfield, Michael J; Montecucco, Cesare

    2018-06-01

    Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes. Copyright © 2018. Published by Elsevier Ltd.

  6. The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

    Science.gov (United States)

    Kunsting, Josef; Wirth, Joachim; Paas, Fred

    2011-01-01

    Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

  7. Mobile STEMship Discovery Center: K-12 Aerospace-Based Science, Technology, Engineering, and Mathematics (STEM) Mobile Teaching Vehicle

    Science.gov (United States)

    2015-08-03

    AND SUBTITLE Mobile STEMship Discovery Center: K-12 Aerospace-Based Science, Technology, Engineering, and Mathematics (STEM) Mobile Teaching Vehicle...Center program to be able to expose Science Technology, Engineering and Mathematics (STEM) space-inspired science centers for DC Metro beltway schools

  8. Towards a routine application of Top-Down approaches for label-free discovery workflows.

    Science.gov (United States)

    Schmit, Pierre-Olivier; Vialaret, Jerome; Wessels, Hans J C T; van Gool, Alain J; Lehmann, Sylvain; Gabelle, Audrey; Wood, Jason; Bern, Marshall; Paape, Rainer; Suckau, Detlev; Kruppa, Gary; Hirtz, Christophe

    2018-03-20

    Thanks to proteomics investigations, our vision of the role of different protein isoforms in the pathophysiology of diseases has largely evolved. The idea that protein biomarkers like tau, amyloid peptides, ApoE, cystatin, or neurogranin are represented in body fluids as single species is obviously over-simplified, as most proteins are present in different isoforms and subjected to numerous processing and post-translational modifications. Measuring the intact mass of proteins by MS has the advantage to provide information on the presence and relative amount of the different proteoforms. Such Top-Down approaches typically require a high degree of sample pre-fractionation to allow the MS system to deliver optimal performance in terms of dynamic range, mass accuracy and resolution. In clinical studies, however, the requirements for pre-analytical robustness and sample size large enough for statistical power restrict the routine use of a high degree of sample pre-fractionation. In this study, we have investigated the capacities of current-generation Ultra-High Resolution Q-Tof systems to deal with high complexity intact protein samples and have evaluated the approach on a cohort of patients suffering from neurodegenerative disease. Statistical analysis has shown that several proteoforms can be used to distinguish Alzheimer disease patients from patients suffering from other neurodegenerative disease. Top-down approaches have an extremely high biological relevance, especially when it comes to biomarker discovery, but the necessary pre-fractionation constraints are not easily compatible with the robustness requirements and the size of clinical sample cohorts. We have demonstrated that intact protein profiling studies could be run on UHR-Q-ToF with limited pre-fractionation. The proteoforms that have been identified as candidate biomarkers in the-proof-of concept study are derived from proteins known to play a role in the pathophysiology process of Alzheimer disease

  9. Towards novel therapeutics for HIV through fragment-based screening and drug design.

    Science.gov (United States)

    Tiefendbrunn, Theresa; Stout, C David

    2014-01-01

    Fragment-based drug discovery has been applied with varying levels of success to a number of proteins involved in the HIV (Human Immunodeficiency Virus) life cycle. Fragment-based approaches have led to the discovery of novel binding sites within protease, reverse transcriptase, integrase, and gp41. Novel compounds that bind to known pockets within CCR5 have also been identified via fragment screening, and a fragment-based approach to target the TAR-Tat interaction was explored. In the context of HIV-1 reverse transcriptase (RT), fragment-based approaches have yielded fragment hits with mid-μM activity in an in vitro activity assay, as well as fragment hits that are active against drug-resistant variants of RT. Fragment-based drug discovery is a powerful method to elucidate novel binding sites within proteins, and the method has had significant success in the context of HIV proteins.

  10. Advancing Drug Discovery through Enhanced Free Energy Calculations.

    Science.gov (United States)

    Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A

    2017-07-18

    A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates

  11. Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

    Science.gov (United States)

    2014-01-01

    Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353

  12. A novel approach to Service Discovery in Mobile Adhoc Network

    OpenAIRE

    Islam, Noman; Shaikh, Zubair A.

    2015-01-01

    Mobile Adhoc Network (MANET) is a network of a number of mobile routers and associated hosts, organized in a random fashion via wireless links. During recent years MANET has gained enormous amount of attention and has been widely used for not only military purposes but for search-and-rescue operations, intelligent transportation system, data collection, virtual classrooms and ubiquitous computing. Service Discovery is one of the most important issues in MANET. It is defined as the process of ...

  13. Scientific workflows as productivity tools for drug discovery.

    Science.gov (United States)

    Shon, John; Ohkawa, Hitomi; Hammer, Juergen

    2008-05-01

    Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.

  14. Cluster-based service discovery for heterogeneous wireless sensor networks

    NARCIS (Netherlands)

    Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

    2007-01-01

    We propose an energy-efficient service discovery protocol for heterogeneous wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the

  15. Rough – Granular Computing knowledge discovery models

    Directory of Open Access Journals (Sweden)

    Mohammed M. Eissa

    2016-11-01

    Full Text Available Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology.

  16. Perbandingan antara Keefektifan Model Guided Discovery Learning dan Project-Based Learning pada Matakuliah Geometri

    Directory of Open Access Journals (Sweden)

    Okky Riswandha Imawan

    2015-12-01

    Abstract This research aims to describe the effectiveness and effectiveness differences of the Guided Discovery Learning (GDL Model and the Project Based Learning (PjBL Model in terms of achievement, self-confidence, and critical thinking skills of students on the Solid Geometry subjects. This research was quasi experimental. The research subjects were two undergraduate classes of Mathematics Education Program, Ahmad Dahlan University, in their second semester, established at random. The data analysis to test the effectiveness of the GDL and PjBL Models in terms of each of the dependent variables used the t-test. The data analysis to test differences between effectiveness of the GDL and that of the PjBL Model used the MANOVA test. The results of this research show that viewed from achievement, self confidence, and critical thinking skills of the students are the application of the GDL Model on Solid Geometry subject is effective, the application of the PjBL Model on Solid Geometry subject is effective, and there is no difference in the effectiveness of GDL and PjBL Models on Solid Geometry subject in terms of achievement, self confidence, and critical thinking skills of the students. Keywords: guided discovery learning model, project-based learning model, achievement, self-confidence, critical thinking skills

  17. A cell-based fluorescent glucose transporter assay for SGLT2 inhibitor discovery

    Directory of Open Access Journals (Sweden)

    Yi Huan

    2013-04-01

    Full Text Available The sodium/glucose cotransporter 2 (SGLT2 is responsible for the majority of glucose reabsorption in the kidney, and currently, SGLT2 inhibitors are considered as promising hypoglycemic agents for the treatment of type 2 diabetes mellitus. By constructing CHO cell lines that stably express the human SGLT2 transmembrane protein, along with a fluorescent glucose transporter assay that uses 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-ylamino]2-deoxyglucose (2-NBDG as a glucose analog, we have developed a nonradioactive, cell-based assay for the discovery and characterization of SGLT2 inhibitors.

  18. Inseparability of science history and discovery

    Directory of Open Access Journals (Sweden)

    J. M. Herndon

    2010-04-01

    Full Text Available Science is very much a logical progression through time. Progressing along a logical path of discovery is rather like following a path through the wilderness. Occasionally the path splits, presenting a choice; the correct logical interpretation leads to further progress, the wrong choice leads to confusion. By considering deeply the relevant science history, one might begin to recognize past faltering in the logical progression of observations and ideas and, perhaps then, to discover new, more precise understanding. The following specific examples of science faltering are described from a historical perspective: (1 Composition of the Earth's inner core; (2 Giant planet internal energy production; (3 Physical impossibility of Earth-core convection and Earth-mantle convection, and; (4 Thermonuclear ignition of stars. For each example, a revised logical progression is described, leading, respectively, to: (1 Understanding the endo-Earth's composition; (2 The concept of nuclear georeactor origin of geo- and planetary magnetic fields; (3 The invalidation and replacement of plate tectonics; and, (4 Understanding the basis for the observed distribution of luminous stars in galaxies. These revised logical progressions clearly show the inseparability of science history and discovery. A different and more fundamental approach to making scientific discoveries than the frequently discussed variants of the scientific method is this: An individual ponders and through tedious efforts arranges seemingly unrelated observations into a logical sequence in the mind so that causal relationships become evident and new understanding emerges, showing the path for new observations, for new experiments, for new theoretical considerations, and for new discoveries. Science history is rich in "seemingly unrelated observations" just waiting to be logically and causally related to reveal new discoveries.

  19. Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

    Directory of Open Access Journals (Sweden)

    Zomaya Albert Y

    2006-12-01

    Full Text Available Abstract Background Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix, secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. Conclusion Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset.

  20. Discovery Learning: Zombie, Phoenix, or Elephant?

    Science.gov (United States)

    Bakker, Arthur

    2018-01-01

    Discovery learning continues to be a topic of heated debate. It has been called a zombie, and this special issue raises the question whether it may be a phoenix arising from the ashes to which the topic was burnt. However, in this commentary I propose it is more like an elephant--a huge topic approached by many people who address different…

  1. Astrobiology, discovery, and societal impact

    CERN Document Server

    Dick, Steven J

    2018-01-01

    The search for life in the universe, once the stuff of science fiction, is now a robust worldwide research program with a well-defined roadmap probing both scientific and societal issues. This volume examines the humanistic aspects of astrobiology, systematically discussing the approaches, critical issues, and implications of discovering life beyond Earth. What do the concepts of life and intelligence, culture and civilization, technology and communication mean in a cosmic context? What are the theological and philosophical implications if we find life - and if we do not? Steven J. Dick argues that given recent scientific findings, the discovery of life in some form beyond Earth is likely and so we need to study the possible impacts of such a discovery and formulate policies to deal with them. The remarkable and often surprising results are presented here in a form accessible to disciplines across the sciences, social sciences, and humanities.

  2. Emerging techniques for the discovery and validation of therapeutic targets for skeletal diseases.

    Science.gov (United States)

    Cho, Christine H; Nuttall, Mark E

    2002-12-01

    Advances in genomics and proteomics have revolutionised the drug discovery process and target validation. Identification of novel therapeutic targets for chronic skeletal diseases is an extremely challenging process based on the difficulty of obtaining high-quality human diseased versus normal tissue samples. The quality of tissue and genomic information obtained from the sample is critical to identifying disease-related genes. Using a genomics-based approach, novel genes or genes with similar homology to existing genes can be identified from cDNA libraries generated from normal versus diseased tissue. High-quality cDNA libraries are prepared from uncontaminated homogeneous cell populations harvested from tissue sections of interest. Localised gene expression analysis and confirmation are obtained through in situ hybridisation or immunohistochemical studies. Cells overexpressing the recombinant protein are subsequently designed for primary cell-based high-throughput assays that are capable of screening large compound banks for potential hits. Afterwards, secondary functional assays are used to test promising compounds. The same overexpressing cells are used in the secondary assay to test protein activity and functionality as well as screen for small-molecule agonists or antagonists. Once a hit is generated, a structure-activity relationship of the compound is optimised for better oral bioavailability and pharmacokinetics allowing the compound to progress into development. Parallel efforts from proteomics, as well as genetics/transgenics, bioinformatics and combinatorial chemistry, and improvements in high-throughput automation technologies, allow the drug discovery process to meet the demands of the medicinal market. This review discusses and illustrates how different approaches are incorporated into the discovery and validation of novel targets and, consequently, the development of potentially therapeutic agents in the areas of osteoporosis and osteoarthritis

  3. Model-driven discovery of underground metabolic functions in Escherichia coli

    DEFF Research Database (Denmark)

    Guzmán, Gabriela I.; Utrilla, José; Nurk, Sergey

    2015-01-01

    -scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence......E, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations....

  4. Biomarker discovery in neurological diseases: a metabolomic approach

    Directory of Open Access Journals (Sweden)

    Afaf El-Ansary

    2009-12-01

    Full Text Available Afaf El-Ansary, Nouf Al-Afaleg, Yousra Al-YafaeeBiochemistry Department, Science College, King Saud University, Riyadh, Saudi ArabiaAbstract: Biomarkers are pharmacological and physiological measurements or specific biochemicals in the body that have a particular molecular feature that makes them useful for measuring the progress of disease or the effects of treatment. Due to the complexity of neurological disorders, it is very difficult to have perfect markers. Brain diseases require plenty of markers to reflect the metabolic impairment of different brain cells. The recent introduction of the metabolomic approach helps the study of neurological diseases based on profiling a multitude of biochemical components related to brain metabolism. This review is a trial to elucidate the possibility to use this approach to identify plasma metabolic markers related to neurological disorders. Previous trials using different metabolomic analyses including nuclear magnetic resonance spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, and capillary electrophoresis will be traced.Keywords: metabolic biomarkers, neurological disorders. metabolome, nuclear magnetic resonance, mass spectrometry, chromatography

  5. 14 CFR 406.143 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Discovery. 406.143 Section 406.143... Transportation Adjudications § 406.143 Discovery. (a) Initiation of discovery. Any party may initiate discovery... after a complaint has been filed. (b) Methods of discovery. The following methods of discovery are...

  6. Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry.

    Science.gov (United States)

    Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John; Oller Moreno, Sergio; Irincheeva, Irina; Valsesia, Armand; Astrup, Arne; Saris, Wim H M; Hager, Jörg; Kussmann, Martin; Dayon, Loïc

    2016-02-05

    The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.

  7. Validity and Practitality of Acid-Base Module Based on Guided Discovery Learning for Senior High School

    Science.gov (United States)

    Yerimadesi; Bayharti; Jannah, S. M.; Lufri; Festiyed; Kiram, Y.

    2018-04-01

    This Research and Development(R&D) aims to produce guided discovery learning based module on topic of acid-base and determine its validity and practicality in learning. Module development used Four D (4-D) model (define, design, develop and disseminate).This research was performed until development stage. Research’s instruments were validity and practicality questionnaires. Module was validated by five experts (three chemistry lecturers of Universitas Negeri Padang and two chemistry teachers of SMAN 9 Padang). Practicality test was done by two chemistry teachers and 30 students of SMAN 9 Padang. Kappa Cohen’s was used to analyze validity and practicality. The average moment kappa was 0.86 for validity and those for practicality were 0.85 by teachers and 0.76 by students revealing high category. It can be concluded that validity and practicality was proven for high school chemistry learning.

  8. A knowledge discovery in databases approach for industrial microgrid planning

    DEFF Research Database (Denmark)

    Gamarra, Carlos; Guerrero, Josep M.; Montero, Eduardo

    2016-01-01

    The progressive application of Information and Communication Technologies to industrial processes has increased the amount of data gathered by manufacturing companies during last decades. Nowadays some standardized management systems, such as ISO 50.001 and ISO 14.001, exploit these data in order...... sustainable and proactive microgrid which allows identifying, designing and developing energy efficiency strategies at supply, management and energy use levels. In this context, the expansion of Internet of Things and Knowledge Discovery in Databases techniques will drive changes in current microgrid planning...

  9. Reflections on the discovery of fission

    International Nuclear Information System (INIS)

    Peieris, Rudolf

    1989-01-01

    In this article an eminent scientist looks back on the fifty years since the discovery of nuclear fission. Starting with Enrico Fermi's work with neutrons in the 1930s, the author then introduces Neils Bohr's ideas about atomic structure. The puzzle of what happens when uranium was bombarded by neutrons was gradually unravelled. Finally by 1939 it was becoming realised that the uranium nucleus had split in two. Gradually physicists began to speculate on the possibility of harnessing some of the energy stored in the nucleus and on the idea of a chain reaction. As the end of the decade approached, workers in the field combined with military forces to develop a weapon based on this reaction, the Manhatton Project. The author notes how fast an obscure, esoteric piece of physics research, can be taken up into the military and political area. (UK)

  10. Higgs Discovery

    DEFF Research Database (Denmark)

    Sannino, Francesco

    2013-01-01

    has been challenged by the discovery of a not-so-heavy Higgs-like state. I will therefore review the recent discovery \\cite{Foadi:2012bb} that the standard model top-induced radiative corrections naturally reduce the intrinsic non-perturbative mass of the composite Higgs state towards the desired...... via first principle lattice simulations with encouraging results. The new findings show that the recent naive claims made about new strong dynamics at the electroweak scale being disfavoured by the discovery of a not-so-heavy composite Higgs are unwarranted. I will then introduce the more speculative......I discuss the impact of the discovery of a Higgs-like state on composite dynamics starting by critically examining the reasons in favour of either an elementary or composite nature of this state. Accepting the standard model interpretation I re-address the standard model vacuum stability within...

  11. Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

    Science.gov (United States)

    Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

    2014-07-01

    Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain- (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

  12. How Phenotypic Screening Influenced Drug Discovery: Lessons from Five Years of Practice.

    Science.gov (United States)

    Haasen, Dorothea; Schopfer, Ulrich; Antczak, Christophe; Guy, Chantale; Fuchs, Florian; Selzer, Paul

    Since 2011, phenotypic screening has been a trend in the pharmaceutical industry as well as in academia. This renaissance was triggered by analyses that suggested that phenotypic screening is a superior strategy to discover first-in-class drugs. Despite these promises and considerable investments, pharmaceutical research organizations have encountered considerable challenges with the approach. Few success stories have emerged in the past 5 years and companies are questioning their investment in this area. In this contribution, we outline what we have learned about success factors and challenges of phenotypic screening. We then describe how our efforts in phenotypic screening have influenced our approach to drug discovery in general. We predict that concepts from phenotypic screening will be incorporated into target-based approaches and will thus remain influential beyond the current trend.

  13. Green Materials Science and Engineering Reduces Biofouling: Approaches for Medical and Membrane-based Technologies

    Directory of Open Access Journals (Sweden)

    Kerianne M Dobosz

    2015-03-01

    Full Text Available Numerous engineered and natural environments suffer deleterious effects from biofouling and/or biofilm formation. For instance, bacterial contamination on biomedical devices pose serious health concerns. In membrane-based technologies, such as desalination and wastewater reuse, biofouling decreases membrane lifetime and increases the energy required to produce clean water. Traditionally, approaches have combatted bacteria using bactericidal agents. However, due to globalization, a decline in antibiotic discovery, and the widespread resistance of microbes to many commercial antibiotics and metallic nanoparticles, new materials and approaches to reduce biofilm formation are needed. In this mini-review, we cover the recent strategies that have been explored to combat microbial contamination without exerting evolutionary pressure on microorganisms. Renewable feedstocks, relying on structure-property relationships, bioinspired/nature-derived compounds, and green processing methods are discussed. Greener strategies that mitigate biofouling hold great potential to positively impact human health and safety.

  14. An evolutionary perspective on drug discovery in the plant genus Euphorbia L. (Euphorbiaceae)

    DEFF Research Database (Denmark)

    Ernst, Madeleine

    herbivory and physical stresses or to attract pollinators. Consequently, specializedmetabolites, as well as plants used in traditional medicine, are not randomly distributed across phylogenetictrees. Evolutionary approaches to plant-based drug discovery suggest that this informationcan be used to guide...... healthcarethreats, urge for systematic and time-efficient approaches in finding new drug candidates. Manydrugs are derived from plant specialized metabolites, chemical compounds, which are synthesizedby the plants in response to evolutionary adaptation to environmental and ecological factors, for example,to combat...... evolution and diversification. Also, Euphorbia species producean often chemically highly diverse latex exhibiting an exceptional number of biological activities withpharmaceutical interest. In this PhD project, the genus Euphorbia was chosen as a model group forstudying evolutionary approaches to plant...

  15. Genome-Based Studies of Marine Microorganisms to Maximize the Diversity of Natural Products Discovery for Medical Treatments

    Directory of Open Access Journals (Sweden)

    Xin-Qing Zhao

    2011-01-01

    Full Text Available Marine microorganisms are rich source for natural products which play important roles in pharmaceutical industry. Over the past decade, genome-based studies of marine microorganisms have unveiled the tremendous diversity of the producers of natural products and also contributed to the efficiency of harness the strain diversity and chemical diversity, as well as the genetic diversity of marine microorganisms for the rapid discovery and generation of new natural products. In the meantime, genomic information retrieved from marine symbiotic microorganisms can also be employed for the discovery of new medical molecules from yet-unculturable microorganisms. In this paper, the recent progress in the genomic research of marine microorganisms is reviewed; new tools of genome mining as well as the advance in the activation of orphan pathways and metagenomic studies are summarized. Genome-based research of marine microorganisms will maximize the biodiscovery process and solve the problems of supply and sustainability of drug molecules for medical treatments.

  16. A role for physicians in ethnopharmacology and drug discovery.

    Science.gov (United States)

    Raza, Mohsin

    2006-04-06

    Ethnopharmacology investigations classically involved traditional healers, botanists, anthropologists, chemists and pharmacologists. The role of some groups of researchers but not of physician has been highlighted and well defined in ethnopharmacological investigations. Historical data shows that discovery of several important modern drugs of herbal origin owe to the medical knowledge and clinical expertise of physicians. Current trends indicate negligible role of physicians in ethnopharmacological studies. Rising cost of modern drug development is attributed to the lack of classical ethnopharmacological approach. Physicians can play multiple roles in the ethnopharmacological studies to facilitate drug discovery as well as to rescue authentic traditional knowledge of use of medicinal plants. These include: (1) Ethnopharmacological field work which involves interviewing healers, interpreting traditional terminologies into their modern counterparts, examining patients consuming herbal remedies and identifying the disease for which an herbal remedy is used. (2) Interpretation of signs and symptoms mentioned in ancient texts and suggesting proper use of old traditional remedies in the light of modern medicine. (3) Clinical studies on herbs and their interaction with modern medicines. (4) Advising pharmacologists to carryout laboratory studies on herbs observed during field studies. (5) Work in collaboration with local healers to strengthen traditional system of medicine in a community. In conclusion, physician's involvement in ethnopharmacological studies will lead to more reliable information on traditional use of medicinal plants both from field and ancient texts, more focused and cheaper natural product based drug discovery, as well as bridge the gap between traditional and modern medicine.

  17. SeLeCT: a lexical cohesion based news story segmentation system

    OpenAIRE

    Stokes, Nicola; Carthy, Joe; Smeaton, Alan F.

    2004-01-01

    In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between ...

  18. Bioinformatics for discovery of microbiome variation

    DEFF Research Database (Denmark)

    Brejnrod, Asker Daniel

    of various molecular methods to build hypotheses about the impact of a copper contaminated soil. The introduction is a broad introduction to the field of microbiome research with a focus on the technologies that enable these discoveries and how some of the broader issues have related to this thesis......Sequencing based tools have revolutionized microbiology in recent years. Highthroughput DNA sequencing have allowed high-resolution studies on microbial life in many different environments and at unprecedented low cost. These culture-independent methods have helped discovery of novel bacteria...... 1 ,“Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies”, benchmarked the performance of a variety of popular statistical methods for discovering differentially abundant bacteria . between...

  19. Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery.

    Science.gov (United States)

    Nagamani, S; Gaur, A S; Tanneeru, K; Muneeswaran, G; Madugula, S S; Consortium, Mpds; Druzhilovskiy, D; Poroikov, V V; Sastry, G N

    2017-11-01

    Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

  20. Discovery and design of carbohydrate-based therapeutics.

    Science.gov (United States)

    Cipolla, Laura; Araújo, Ana C; Bini, Davide; Gabrielli, Luca; Russo, Laura; Shaikh, Nasrin

    2010-08-01

    Till now, the importance of carbohydrates has been underscored, if compared with the two other major classes of biopolymers such as oligonucleotides and proteins. Recent advances in glycobiology and glycochemistry have imparted a strong interest in the study of this enormous family of biomolecules. Carbohydrates have been shown to be implicated in recognition processes, such as cell-cell adhesion, cell-extracellular matrix adhesion and cell-intruder recognition phenomena. In addition, carbohydrates are recognized as differentiation markers and as antigenic determinants. Due to their relevant biological role, carbohydrates are promising candidates for drug design and disease treatment. However, the growing number of human disorders known as congenital disorders of glycosylation that are being identified as resulting from abnormalities in glycan structures and protein glycosylation strongly indicates that a fast development of glycobiology, glycochemistry and glycomedicine is highly desirable. The topics give an overview of different approaches that have been used to date for the design of carbohydrate-based therapeutics; this includes the use of native synthetic carbohydrates, the use of carbohydrate mimics designed on the basis of their native counterpart, the use of carbohydrates as scaffolds and finally the design of glyco-fused therapeutics, one of the most recent approaches. The review covers mainly literature that has appeared since 2000, except for a few papers cited for historical reasons. The reader will gain an overview of the current strategies applied to the design of carbohydrate-based therapeutics; in particular, the advantages/disadvantages of different approaches are highlighted. The topic is presented in a general, basic manner and will hopefully be a useful resource for all readers who are not familiar with it. In addition, in order to stress the potentialities of carbohydrates, several examples of carbohydrate-based marketed therapeutics are given

  1. Structure-based drug discovery for combating influenza virus by targeting the PA-PB1 interaction.

    Science.gov (United States)

    Watanabe, Ken; Ishikawa, Takeshi; Otaki, Hiroki; Mizuta, Satoshi; Hamada, Tsuyoshi; Nakagaki, Takehiro; Ishibashi, Daisuke; Urata, Shuzo; Yasuda, Jiro; Tanaka, Yoshimasa; Nishida, Noriyuki

    2017-08-25

    Influenza virus infections are serious public health concerns throughout the world. The development of compounds with novel mechanisms of action is urgently required due to the emergence of viruses with resistance to the currently-approved anti-influenza viral drugs. We performed in silico screening using a structure-based drug discovery algorithm called Nagasaki University Docking Engine (NUDE), which is optimised for a GPU-based supercomputer (DEstination for Gpu Intensive MAchine; DEGIMA), by targeting influenza viral PA protein. The compounds selected by NUDE were tested for anti-influenza virus activity using a cell-based assay. The most potent compound, designated as PA-49, is a medium-sized quinolinone derivative bearing a tetrazole moiety, and it inhibited the replication of influenza virus A/WSN/33 at a half maximal inhibitory concentration of 0.47 μM. PA-49 has the ability to bind PA and its anti-influenza activity was promising against various influenza strains, including a clinical isolate of A(H1N1)pdm09 and type B viruses. The docking simulation suggested that PA-49 interrupts the PA-PB1 interface where important amino acids are mostly conserved in the virus strains tested, suggesting the strain independent utility. Because our NUDE/DEGIMA system is rapid and efficient, it may help effective drug discovery against the influenza virus and other emerging viruses.

  2. Mass spectrometry imaging enriches biomarker discovery approaches with candidate mapping.

    Science.gov (United States)

    Scott, Alison J; Jones, Jace W; Orschell, Christie M; MacVittie, Thomas J; Kane, Maureen A; Ernst, Robert K

    2014-01-01

    Integral to the characterization of radiation-induced tissue damage is the identification of unique biomarkers. Biomarker discovery is a challenging and complex endeavor requiring both sophisticated experimental design and accessible technology. The resources within the National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Consortium, Medical Countermeasures Against Radiological Threats (MCART), allow for leveraging robust animal models with novel molecular imaging techniques. One such imaging technique, MALDI (matrix-assisted laser desorption ionization) mass spectrometry imaging (MSI), allows for the direct spatial visualization of lipids, proteins, small molecules, and drugs/drug metabolites-or biomarkers-in an unbiased manner. MALDI-MSI acquires mass spectra directly from an intact tissue slice in discrete locations across an x, y grid that are then rendered into a spatial distribution map composed of ion mass and intensity. The unique mass signals can be plotted to generate a spatial map of biomarkers that reflects pathology and molecular events. The crucial unanswered questions that can be addressed with MALDI-MSI include identification of biomarkers for radiation damage that reflect the response to radiation dose over time and the efficacy of therapeutic interventions. Techniques in MALDI-MSI also enable integration of biomarker identification among diverse animal models. Analysis of early, sublethally irradiated tissue injury samples from diverse mouse tissues (lung and ileum) shows membrane phospholipid signatures correlated with histological features of these unique tissues. This paper will discuss the application of MALDI-MSI for use in a larger biomarker discovery pipeline.

  3. Directional genomic hybridization for chromosomal inversion discovery and detection.

    Science.gov (United States)

    Ray, F Andrew; Zimmerman, Erin; Robinson, Bruce; Cornforth, Michael N; Bedford, Joel S; Goodwin, Edwin H; Bailey, Susan M

    2013-04-01

    Chromosomal rearrangements are a source of structural variation within the genome that figure prominently in human disease, where the importance of translocations and deletions is well recognized. In principle, inversions-reversals in the orientation of DNA sequences within a chromosome-should have similar detrimental potential. However, the study of inversions has been hampered by traditional approaches used for their detection, which are not particularly robust. Even with significant advances in whole genome approaches, changes in the absolute orientation of DNA remain difficult to detect routinely. Consequently, our understanding of inversions is still surprisingly limited, as is our appreciation for their frequency and involvement in human disease. Here, we introduce the directional genomic hybridization methodology of chromatid painting-a whole new way of looking at structural features of the genome-that can be employed with high resolution on a cell-by-cell basis, and demonstrate its basic capabilities for genome-wide discovery and targeted detection of inversions. Bioinformatics enabled development of sequence- and strand-specific directional probe sets, which when coupled with single-stranded hybridization, greatly improved the resolution and ease of inversion detection. We highlight examples of the far-ranging applicability of this cytogenomics-based approach, which include confirmation of the alignment of the human genome database and evidence that individuals themselves share similar sequence directionality, as well as use in comparative and evolutionary studies for any species whose genome has been sequenced. In addition to applications related to basic mechanistic studies, the information obtainable with strand-specific hybridization strategies may ultimately enable novel gene discovery, thereby benefitting the diagnosis and treatment of a variety of human disease states and disorders including cancer, autism, and idiopathic infertility.

  4. The rays of life, centennial of discovery of Radium

    International Nuclear Information System (INIS)

    Constantin, Enrique; Plazas, Maria C.

    1999-01-01

    The authors make a recount from the discovery of the rays X for William Conrad Roentgen, in November of 1.985, until our days of the main discoveries and advances in medicine, having like base the radium and their importance in the treatment of the cancer

  5. GEOSS authentication/authorization services: a Broker-based approach

    Science.gov (United States)

    Santoro, M.; Nativi, S.

    2014-12-01

    The vision of the Global Earth Observation System of Systems (GEOSS) is the achievement of societal benefits through voluntary contribution and sharing of resources to better understand the relationships between the society and the environment where we live. The GEOSS Common Infrastructure (GCI) allows users to search, access, and use the resources contributed by the GEOSS members. The GEO DAB (Discovery and Access Broker) is the GCI component in charge of interconnecting the heterogeneous data systems contributing to GEOSS. Client applications (i.e. the portals and apps) can connect to GEO DAB as a unique entry point to discover and access resources available through GCI, with no need to implement the many service protocols and models applied by the GEOSS data providers. The GEO DAB implements the brokering approach (Nativi et al., 2013) to build a flexible and scalable System of Systems. User authentication/authorization functionality is becoming more and more important for GEOSS data providers and users. The Providers ask for information about who accessed their resources and, in some cases, want to limit the data download. The Users ask for a profiled interaction with the system based on their needs and expertise level. Besides, authentication and authorization is necessary for GEOSS to provide moderated social services - e.g. feedback messages, data "fit for use" comments, etc. In keeping with the GEOSS principles of building on existing systems and lowering entry-barriers for users, an objective of the authentication/authorization development was to support existing and well-used users' credentials (e.g. Google, Twitter, etc.). Due to the heterogeneity of technologies used by the different providers and applications, a broker-based approach for the authentication/authorization was introduced as a new functionality of GEO DAB. This new capability will be demonstrated at the next GEO XI Plenary (November 2014). This work will be presented and discussed

  6. A Novel Approach to Discovery and Image Access in the Petabyte Age

    Science.gov (United States)

    Mueller, D.; Dimitoglou, G.; Alexanderian, A.; Garcia Ortiz, J.; Schmidt, L.; Hughitt, V. K.; Ireland, J.; Fleck, B.

    2009-12-01

    Space missions generate an ever-growing amount of data, as impressively highlighted by the Solar Dynamics Observatory's expected data rate of 1.4 Terabyte per day. In order to fully exploit their data, scientists need to be able to browse and visualize many different data products spanning a large range of physical length and time scales. So far, the tools available to the scientific community either require downloading all potentially relevant data sets beforehand in their entirety or provide only movies with a fixed resolution and cadence. To facilitate browsing and analysis of complex time-dependent data sets from multiple sources, we are developing JHelioviewer, a JPEG 2000-based visualization and discovery infrastructure for image data. Currently focused on solar physics data, it can easily be adapted for use in other areas of space and earth sciences. Together with its web-based counterpart helioviewer.org, JHelioviewer offers intuitive ways to browse large amounts of heterogeneous data remotely and allows users to search related event data bases. The user interface for JHelioviewer is a multi-platform Java client that can both communicate with a remote server via the JPEG 2000 interactive protocol JPIP and open local data. The random code stream access of JPIP minimizes data transfer and can encapsulate meta data as well as multiple image channels in one data stream. This presentation will illustrate some of the features of JHelioviewer and the advantages of JPEG 2000 as a new data compression standard.

  7. The discovery of the periodic table as a case of simultaneous discovery.

    Science.gov (United States)

    Scerri, Eric

    2015-03-13

    The article examines the question of priority and simultaneous discovery in the context of the discovery of the periodic system. It is argued that rather than being anomalous, simultaneous discovery is the rule. Moreover, I argue that the discovery of the periodic system by at least six authors in over a period of 7 years represents one of the best examples of a multiple discovery. This notion is supported by a new view of the evolutionary development of science through a mechanism that is dubbed Sci-Gaia by analogy with Lovelock's Gaia hypothesis. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  8. BPS Pharmacology 2014 - Drug Discovery Pathways symposium Report

    OpenAIRE

    Marsh, Andrew

    2015-01-01

    Report on BPS Pharmacology 2014, BPS Industry Committe and Learned Societies Drug Discovery Pathways Group symposium: "Realizing the potential of new approaches to target identification and validation" by Dr Andrew Marsh Associate Professor Department of Chemistry University of Warwick go.warwick.ac.uk/marshgroup Twitter @marshgroup

  9. Beyond Discovery

    DEFF Research Database (Denmark)

    Korsgaard, Steffen; Sassmannshausen, Sean Patrick

    2017-01-01

    In this chapter we explore four alternatives to the dominant discovery view of entrepreneurship; the development view, the construction view, the evolutionary view, and the Neo-Austrian view. We outline the main critique points of the discovery presented in these four alternatives, as well...

  10. Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures

    DEFF Research Database (Denmark)

    Stark, Alexander; Lin, Michael F; Kheradpour, Pouya

    2007-01-01

    Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional e...... individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies....

  11. Librarian-Initiated Publications Discovery: How Do Digital Depository Librarians Discover and Select Web-Based Government Publications for State Digital Depositories?

    Science.gov (United States)

    Lin, Chi-Shiou; Eschenfelder, Kristin R.

    2010-01-01

    This paper reports on a study of librarian initiated publications discovery (LIPD) in U.S. state digital depository programs using the OCLC Digital Archive to preserve web-based government publications for permanent public access. This paper describes a model of LIPD processes based on empirical investigations of four OCLC DA-based digital…

  12. Developing integrated crop knowledge networks to advance candidate gene discovery.

    Science.gov (United States)

    Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

    2016-12-01

    The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

  13. Translational Research 2.0: a framework for accelerating collaborative discovery.

    Science.gov (United States)

    Asakiewicz, Chris

    2014-05-01

    The world wide web has revolutionized the conduct of global, cross-disciplinary research. In the life sciences, interdisciplinary approaches to problem solving and collaboration are becoming increasingly important in facilitating knowledge discovery and integration. Web 2.0 technologies promise to have a profound impact - enabling reproducibility, aiding in discovery, and accelerating and transforming medical and healthcare research across the healthcare ecosystem. However, knowledge integration and discovery require a consistent foundation upon which to operate. A foundation should be capable of addressing some of the critical issues associated with how research is conducted within the ecosystem today and how it should be conducted for the future. This article will discuss a framework for enhancing collaborative knowledge discovery across the medical and healthcare research ecosystem. A framework that could serve as a foundation upon which ecosystem stakeholders can enhance the way data, information and knowledge is created, shared and used to accelerate the translation of knowledge from one area of the ecosystem to another.

  14. "Eureka, Eureka!" Discoveries in Science

    Science.gov (United States)

    Agarwal, Pankaj

    2011-01-01

    Accidental discoveries have been of significant value in the progress of science. Although accidental discoveries are more common in pharmacology and chemistry, other branches of science have also benefited from such discoveries. While most discoveries are the result of persistent research, famous accidental discoveries provide a fascinating…

  15. AM: An Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search

    Science.gov (United States)

    1976-07-01

    deficiency . The idea of "Intuitions" facets was a flop. Intuitions were meant to model reality, at least little pieces of it, so that AM could...Discovery in Mathematic, as Heuristic Search -323- s Tk2 ** Check examples of Single-ADD, because many examples have recently been found, but not yet

  16. 30 CFR 44.24 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Discovery. 44.24 Section 44.24 Mineral... Discovery. Parties shall be governed in their conduct of discovery by appropriate provisions of the Federal... discovery. Alternative periods of time for discovery may be prescribed by the presiding administrative law...

  17. 19 CFR 356.20 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery. 356.20 Section 356.20 Customs Duties... § 356.20 Discovery. (a) Voluntary discovery. All parties are encouraged to engage in voluntary discovery... sanctions proceeding. (b) Limitations on discovery. The administrative law judge shall place such limits...

  18. Chemical Discovery

    Science.gov (United States)

    Brown, Herbert C.

    1974-01-01

    The role of discovery in the advance of the science of chemistry and the factors that are currently operating to handicap that function are considered. Examples are drawn from the author's work with boranes. The thesis that exploratory research and discovery should be encouraged is stressed. (DT)

  19. An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts.

    Science.gov (United States)

    Yoo, Sang Wook; Guevara, Pamela; Jeong, Yong; Yoo, Kwangsun; Shin, Joseph S; Mangin, Jean-Francois; Seong, Joon-Kyung

    2015-01-01

    We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

  20. Changing the Scale and Efficiency of Chemical Warfare Countermeasure Discovery Using the Zebrafish

    Science.gov (United States)

    Peterson, Randall T.; MacRae, Calum A.

    2013-01-01

    As the scope of potential chemical warfare agents grows rapidly and as the diversity of potential threat scenarios expands with non-state actors, so a need for innovative approaches to countermeasure development has emerged. In the last few years, the utility of the zebrafish as a model organism that is amenable to high-throughput screening has become apparent and this system has been applied to the unbiased discovery of chemical warfare countermeasures. This review summarizes the in vivo screening approach that has been pioneered in the countermeasure discovery arena, and highlights the successes to date as well as the potential challenges in moving the field forward. Importantly, the establishment of a zebrafish platform for countermeasure discovery would offer a rapid response system for the development of antidotes to the continuous stream of new potential chemical warfare agents. PMID:24273586

  1. 24 CFR 180.500 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Discovery. 180.500 Section 180.500... OPPORTUNITY CONSOLIDATED HUD HEARING PROCEDURES FOR CIVIL RIGHTS MATTERS Discovery § 180.500 Discovery. (a) In general. This subpart governs discovery in aid of administrative proceedings under this part. Discovery in...

  2. 22 CFR 224.21 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Discovery. 224.21 Section 224.21 Foreign....21 Discovery. (a) The following types of discovery are authorized: (1) Requests for production of... parties, discovery is available only as ordered by the ALJ. The ALJ shall regulate the timing of discovery...

  3. Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

    Directory of Open Access Journals (Sweden)

    Dario Gioia

    2017-11-01

    Full Text Available Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking. Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

  4. PERSONAL AND CIRCUMSTANTIAL FACTORS INFLUENCING THE ACT OF DISCOVERY.

    Science.gov (United States)

    OSTRANDER, EDWARD R.

    HOW STUDENTS SAY THEY LEARN WAS INVESTIGATED. INTERVIEWS WITH A RANDOM SAMPLE OF 74 WOMEN STUDENTS POSED QUESTIONS ABOUT THE NATURE, FREQUENCY, PATTERNS, AND CIRCUMSTANCES UNDER WHICH ACTS OF DISCOVERY TAKE PLACE IN THE ACADEMIC SETTING. STUDENTS WERE ASSIGNED DISCOVERY RATINGS BASED ON READINGS OF TYPESCRIPTS. EACH STUDENT WAS CLASSIFIED AND…

  5. A Hybrid Information Mining Approach for Knowledge Discovery in Cardiovascular Disease (CVD

    Directory of Open Access Journals (Sweden)

    Stefania Pasanisi

    2018-04-01

    Full Text Available The healthcare ambit is usually perceived as “information rich” yet “knowledge poor”. Nowadays, an unprecedented effort is underway to increase the use of business intelligence techniques to solve this problem. Heart disease (HD is a major cause of mortality in modern society. This paper analyzes the risk factors that have been identified in cardiovascular disease (CVD surveillance systems. The Heart Care study identifies attributes related to CVD risk (gender, age, smoking habit, etc. and other dependent variables that include a specific form of CVD (diabetes, hypertension, cardiac disease, etc.. In this paper, we combine Clustering, Association Rules, and Neural Networks for the assessment of heart-event-related risk factors, targeting the reduction of CVD risk. With the use of the K-means algorithm, significant groups of patients are found. Then, the Apriori algorithm is applied in order to understand the kinds of relations between the attributes within the dataset, first looking within the whole dataset and then refining the results through the subsets defined by the clusters. Finally, both results allow us to better define patients’ characteristics in order to make predictions about CVD risk with a Multilayer Perceptron Neural Network. The results obtained with the hybrid information mining approach indicate that it is an effective strategy for knowledge discovery concerning chronic diseases, particularly for CVD risk.

  6. iSyTE 2.0: a database for expression-based gene discovery in the eye

    Science.gov (United States)

    Kakrana, Atul; Yang, Andrian; Anand, Deepti; Djordjevic, Djordje; Ramachandruni, Deepti; Singh, Abhyudai; Huang, Hongzhan

    2018-01-01

    Abstract Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches. PMID:29036527

  7. Recent advances in inkjet dispensing technologies: applications in drug discovery.

    Science.gov (United States)

    Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin

    2012-09-01

    Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug discovery.

  8. 19 CFR 207.109 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery. 207.109 Section 207.109 Customs Duties... and Committee Proceedings § 207.109 Discovery. (a) Discovery methods. All parties may obtain discovery under such terms and limitations as the administrative law judge may order. Discovery may be by one or...

  9. 15 CFR 25.21 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Discovery. 25.21 Section 25.21... Discovery. (a) The following types of discovery are authorized: (1) Requests for production of documents for..., discovery is available only as ordered by the ALJ. The ALJ shall regulate the timing of discovery. (d...

  10. Computer-aided drug discovery [v1; ref status: indexed, http://f1000r.es/5ij

    Directory of Open Access Journals (Sweden)

    Jürgen Bajorath

    2015-08-01

    Full Text Available Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

  11. Phylogeny based discovery of regulatory elements

    Directory of Open Access Journals (Sweden)

    Cohen Barak A

    2006-05-01

    Full Text Available Abstract Background Algorithms that locate evolutionarily conserved sequences have become powerful tools for finding functional DNA elements, including transcription factor binding sites; however, most methods do not take advantage of an explicit model for the constrained evolution of functional DNA sequences. Results We developed a probabilistic framework that combines an HKY85 model, which assigns probabilities to different base substitutions between species, and weight matrix models of transcription factor binding sites, which describe the probabilities of observing particular nucleotides at specific positions in the binding site. The method incorporates the phylogenies of the species under consideration and takes into account the position specific variation of transcription factor binding sites. Using our framework we assessed the suitability of alignments of genomic sequences from commonly used species as substrates for comparative genomic approaches to regulatory motif finding. We then applied this technique to Saccharomyces cerevisiae and related species by examining all possible six base pair DNA sequences (hexamers and identifying sequences that are conserved in a significant number of promoters. By combining similar conserved hexamers we reconstructed known cis-regulatory motifs and made predictions of previously unidentified motifs. We tested one prediction experimentally, finding it to be a regulatory element involved in the transcriptional response to glucose. Conclusion The experimental validation of a regulatory element prediction missed by other large-scale motif finding studies demonstrates that our approach is a useful addition to the current suite of tools for finding regulatory motifs.

  12. Scientific Prediction and Prophetic Patenting in Drug Discovery.

    Science.gov (United States)

    Curry, Stephen H; Schneiderman, Anne M

    2015-01-01

    Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.

  13. MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development.

    Science.gov (United States)

    Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

    2015-01-01

    Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/.

  14. Proteomics for discovery of candidate colorectal cancer biomarkers

    Science.gov (United States)

    Álvarez-Chaver, Paula; Otero-Estévez, Olalla; Páez de la Cadena, María; Rodríguez-Berrocal, Francisco J; Martínez-Zorzano, Vicenta S

    2014-01-01

    Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers. PMID:24744574

  15. Systems biology-embedded target validation: improving efficacy in drug discovery.

    Science.gov (United States)

    Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter

    2014-01-01

    The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.

  16. 39 CFR 963.14 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 39 Postal Service 1 2010-07-01 2010-07-01 false Discovery. 963.14 Section 963.14 Postal Service... PANDERING ADVERTISEMENTS STATUTE, 39 U.S.C. 3008 § 963.14 Discovery. Discovery is to be conducted on a... such discovery as he or she deems reasonable and necessary. Discovery may include one or more of the...

  17. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

    Science.gov (United States)

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

  18. The analysis of student’s critical thinking ability on discovery learning by using hand on activity based on the curiosity

    Science.gov (United States)

    Sulistiani, E.; Waluya, S. B.; Masrukan

    2018-03-01

    This study aims to determine (1) the effectiveness of Discovery Learning model by using Hand on Activity toward critical thinking abilities, and (2) to describe students’ critical thinking abilities in Discovery Learning by Hand on Activity based on curiosity. This study is mixed method research with concurrent embedded design. Sample of this study are students of VII A and VII B of SMP Daarul Qur’an Ungaran. While the subject in this study is based on the curiosity of the students groups are classified Epistemic Curiosity (EC) and Perceptual Curiosity (PC). The results showed that the learning of Discovery Learning by using Hand on Activity is effective toward mathematics critical thinking abilities. Students of the EC type are able to complete six indicators of mathematics critical thinking abilities, although there are still two indicators that the result is less than the maximum. While students of PC type have not fully been able to complete the indicator of mathematics critical thinking abilities. They are only strong on indicators formulating questions, while on the other five indicators they are still weak. The critical thinking abilities of EC’s students is better than the critical thinking abilities of the PC’s students.

  19. NMR in structure-based drug design.

    Science.gov (United States)

    Carneiro, Marta G; Ab, Eiso; Theisgen, Stephan; Siegal, Gregg

    2017-11-08

    NMR spectroscopy is a powerful technique that can provide valuable structural information for drug discovery endeavors. Here, we discuss the strengths (and limitations) of NMR applications to structure-based drug discovery, highlighting the different levels of resolution and throughput obtainable. Additionally, the emerging field of paramagnetic NMR in drug discovery and recent developments in approaches to speed up and automate protein-observed NMR data collection and analysis are discussed. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

  20. Improved accuracy of supervised CRM discovery with interpolated Markov models and cross-species comparison.

    Science.gov (United States)

    Kazemian, Majid; Zhu, Qiyun; Halfon, Marc S; Sinha, Saurabh

    2011-12-01

    Despite recent advances in experimental approaches for identifying transcriptional cis-regulatory modules (CRMs, 'enhancers'), direct empirical discovery of CRMs for all genes in all cell types and environmental conditions is likely to remain an elusive goal. Effective methods for computational CRM discovery are thus a critically needed complement to empirical approaches. However, existing computational methods that search for clusters of putative binding sites are ineffective if the relevant TFs and/or their binding specificities are unknown. Here, we provide a significantly improved method for 'motif-blind' CRM discovery that does not depend on knowledge or accurate prediction of TF-binding motifs and is effective when limited knowledge of functional CRMs is available to 'supervise' the search. We propose a new statistical method, based on 'Interpolated Markov Models', for motif-blind, genome-wide CRM discovery. It captures the statistical profile of variable length words in known CRMs of a regulatory network and finds candidate CRMs that match this profile. The method also uses orthologs of the known CRMs from closely related genomes. We perform in silico evaluation of predicted CRMs by assessing whether their neighboring genes are enriched for the expected expression patterns. This assessment uses a novel statistical test that extends the widely used Hypergeometric test of gene set enrichment to account for variability in intergenic lengths. We find that the new CRM prediction method is superior to existing methods. Finally, we experimentally validate 12 new CRM predictions by examining their regulatory activity in vivo in Drosophila; 10 of the tested CRMs were found to be functional, while 6 of the top 7 predictions showed the expected activity patterns. We make our program available as downloadable source code, and as a plugin for a genome browser installed on our servers. © The Author(s) 2011. Published by Oxford University Press.

  1. An investigation of the effects of relevant samples and a comparison of verification versus discovery based lab design

    Science.gov (United States)

    Rieben, James C., Jr.

    This study focuses on the effects of relevance and lab design on student learning within the chemistry laboratory environment. A general chemistry conductivity of solutions experiment and an upper level organic chemistry cellulose regeneration experiment were employed. In the conductivity experiment, the two main variables studied were the effect of relevant (or "real world") samples on student learning and a verification-based lab design versus a discovery-based lab design. With the cellulose regeneration experiment, the effect of a discovery-based lab design vs. a verification-based lab design was the sole focus. Evaluation surveys consisting of six questions were used at three different times to assess student knowledge of experimental concepts. In the general chemistry laboratory portion of this study, four experimental variants were employed to investigate the effect of relevance and lab design on student learning. These variants consisted of a traditional (or verification) lab design, a traditional lab design using "real world" samples, a new lab design employing real world samples/situations using unknown samples, and the new lab design using real world samples/situations that were known to the student. Data used in this analysis were collected during the Fall 08, Winter 09, and Fall 09 terms. For the second part of this study a cellulose regeneration experiment was employed to investigate the effects of lab design. A demonstration creating regenerated cellulose "rayon" was modified and converted to an efficient and low-waste experiment. In the first variant students tested their products and verified a list of physical properties. In the second variant, students filled in a blank physical property chart with their own experimental results for the physical properties. Results from the conductivity experiment show significant student learning of the effects of concentration on conductivity and how to use conductivity to differentiate solution types with the

  2. Engineering Application Way of Faults Knowledge Discovery Based on Rough Set Theory

    International Nuclear Information System (INIS)

    Zhao Rongzhen; Deng Linfeng; Li Chao

    2011-01-01

    For the knowledge acquisition puzzle of intelligence decision-making technology in mechanical industry, to use the Rough Set Theory (RST) as a kind of tool to solve the puzzle was researched. And the way to realize the knowledge discovery in engineering application is explored. A case extracting out the knowledge rules from a concise data table shows out some important information. It is that the knowledge discovery similar to the mechanical faults diagnosis is an item of complicated system engineering project. In where, first of all-important tasks is to preserve the faults knowledge into a table with data mode. And the data must be derived from the plant site and should also be as concise as possible. On the basis of the faults knowledge data obtained so, the methods and algorithms to process the data and extract the knowledge rules from them by means of RST can be processed only. The conclusion is that the faults knowledge discovery by the way is a process of rising upward. But to develop the advanced faults diagnosis technology by the way is a large-scale knowledge engineering project for long time. Every step in which should be designed seriously according to the tool's demands firstly. This is the basic guarantees to make the knowledge rules obtained have the values of engineering application and the studies have scientific significance. So, a general framework is designed for engineering application to go along the route developing the faults knowledge discovery technology.

  3. Establishing MALDI-TOF as Versatile Drug Discovery Readout to Dissect the PTP1B Enzymatic Reaction.

    Science.gov (United States)

    Winter, Martin; Bretschneider, Tom; Kleiner, Carola; Ries, Robert; Hehn, Jörg P; Redemann, Norbert; Luippold, Andreas H; Bischoff, Daniel; Büttner, Frank H

    2018-07-01

    Label-free, mass spectrometric (MS) detection is an emerging technology in the field of drug discovery. Unbiased deciphering of enzymatic reactions is a proficient advantage over conventional label-based readouts suffering from compound interference and intricate generation of tailored signal mediators. Significant evolvements of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, as well as associated liquid handling instrumentation, triggered extensive efforts in the drug discovery community to integrate the comprehensive MS readout into the high-throughput screening (HTS) portfolio. Providing speed, sensitivity, and accuracy comparable to those of conventional, label-based readouts, combined with merits of MS-based technologies, such as label-free parallelized measurement of multiple physiological components, emphasizes the advantages of MALDI-TOF for HTS approaches. Here we describe the assay development for the identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors. In the context of this precious drug target, MALDI-TOF was integrated into the HTS environment and cross-compared with the well-established AlphaScreen technology. We demonstrate robust and accurate IC 50 determination with high accordance to data generated by AlphaScreen. Additionally, a tailored MALDI-TOF assay was developed to monitor compound-dependent, irreversible modification of the active cysteine of PTP1B. Overall, the presented data proves the promising perspective for the integration of MALDI-TOF into drug discovery campaigns.

  4. GrandBase: generating actionable knowledge from Big Data

    Directory of Open Access Journals (Sweden)

    Xiu Susie Fang

    2017-08-01

    Full Text Available Purpose – This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB, called GrandBase. Design/methodology/approach – In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase. In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings – Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value – To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem. Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

  5. Accelerating the discovery of materials for clean energy in the era of smart automation

    Science.gov (United States)

    Tabor, Daniel P.; Roch, Loïc M.; Saikin, Semion K.; Kreisbeck, Christoph; Sheberla, Dennis; Montoya, Joseph H.; Dwaraknath, Shyam; Aykol, Muratahan; Ortiz, Carlos; Tribukait, Hermann; Amador-Bedolla, Carlos; Brabec, Christoph J.; Maruyama, Benji; Persson, Kristin A.; Aspuru-Guzik, Alán

    2018-05-01

    The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

  6. Discovery and optimization of peptide-based anti-cobratoxins

    DEFF Research Database (Denmark)

    Sola, M.; Laustsen, Andreas Hougaard; Johannesen, J.

    More than 5.5 million people per year are victims of snake envenomation, resulting in 125,000 deaths and 400,000amputations worldwide. Antivenoms are still produced by animal immunization procedures, and they areassociated with a high risk of severe adverse reactions. Alternatively, synthetic pep...... peptides may open the possibility for newtherapies with better efficacy and safety. Here, we report the discovery and optimization of a synthetic peptide directedagainst α-cobratoxin (α-CTX), the most toxic component of Monocled cobra (Naja kaouthia)....

  7. Energy-Efficient Cluster-Based Service Discovery in Wireless Sensor Networks

    NARCIS (Netherlands)

    Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

    We propose an energy-efficient service discovery protocol for wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication

  8. Energy-Efficient Cluster-Based Service Discovery in Wireless Sensor Networks

    NARCIS (Netherlands)

    Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

    2006-01-01

    We propose an energy-efficient service discovery protocol for wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication

  9. Simulated JWST/NIRISS Transit Spectroscopy of Anticipated Tess Planets Compared to Select Discoveries from Space-based and Ground-based Surveys

    Science.gov (United States)

    Louie, Dana R.; Deming, Drake; Albert, Loic; Bouma, L. G.; Bean, Jacob; Lopez-Morales, Mercedes

    2018-04-01

    The Transiting Exoplanet Survey Satellite (TESS) will embark in 2018 on a 2 year wide-field survey mission, discovering over a thousand terrestrial, super-Earth and sub-Neptune-sized exoplanets ({R}pl}≤slant 4 {R}\\oplus ) potentially suitable for follow-up observations using the James Webb Space Telescope (JWST). This work aims to understand the suitability of anticipated TESS planet discoveries for atmospheric characterization by JWST’s Near InfraRed Imager and Slitless Spectrograph (NIRISS) by employing a simulation tool to estimate the signal-to-noise (S/N) achievable in transmission spectroscopy. We applied this tool to Monte Carlo predictions of the TESS expected planet yield and then compared the S/N for anticipated TESS discoveries to our estimates of S/N for 18 known exoplanets. We analyzed the sensitivity of our results to planetary composition, cloud cover, and presence of an observational noise floor. We find that several hundred anticipated TESS discoveries with radii 1.5 {R}\\oplus R}pl}≤slant 2.5 {R}\\oplus will produce S/N higher than currently known exoplanets in this radius regime, such as K2-3b or K2-3c. In the terrestrial planet regime, we find that only a few anticipated TESS discoveries will result in higher S/N than currently known exoplanets, such as the TRAPPIST-1 planets, GJ1132b, and LHS1140b. However, we emphasize that this outcome is based upon Kepler-derived occurrence rates, and that co-planar compact multi-planet systems (e.g., TRAPPIST-1) may be under-represented in the predicted TESS planet yield. Finally, we apply our calculations to estimate the required magnitude of a JWST follow-up program devoted to mapping the transition region between hydrogen-dominated and high molecular weight atmospheres. We find that a modest observing program of between 60 and 100 hr of charged JWST time can define the nature of that transition (e.g., step function versus a power law).

  10. Mining the Proteome of subsp. ATCC 25586 for Potential Therapeutics Discovery: An Approach

    Directory of Open Access Journals (Sweden)

    Abdul Musaweer Habib

    2016-12-01

    Full Text Available The plethora of genome sequence information of bacteria in recent times has ushered in many novel strategies for antibacterial drug discovery and facilitated medical science to take up the challenge of the increasing resistance of pathogenic bacteria to current antibiotics. In this study, we adopted subtractive genomics approach to analyze the whole genome sequence of the Fusobacterium nucleatum, a human oral pathogen having association with colorectal cancer. Our study divulged 1,499 proteins of F. nucleatum, which have no homolog's in human genome. These proteins were subjected to screening further by using the Database of Essential Genes (DEG that resulted in the identification of 32 vitally important proteins for the bacterium. Subsequent analysis of the identified pivotal proteins, using the Kyoto Encyclopedia of Genes and Genomes (KEGG Automated Annotation Server (KAAS resulted in sorting 3 key enzymes of F. nucleatum that may be good candidates as potential drug targets, since they are unique for the bacterium and absent in humans. In addition, we have demonstrated the three dimensional structure of these three proteins. Finally, determination of ligand binding sites of the 2 key proteins as well as screening for functional inhibitors that best fitted with the ligands sites were conducted to discover effective novel therapeutic compounds against F. nucleatum.

  11. Anthelmintics: From discovery to resistance II (San Diego, 2016

    Directory of Open Access Journals (Sweden)

    Richard J. Martin

    2016-12-01

    Full Text Available The second scientific meeting in the series: “Anthelmintics: From Discovery to Resistance” was held in San Diego in February, 2016. The focus topics of the meeting, related to anthelmintic discovery and resistance, were novel technologies, bioinformatics, commercial interests, anthelmintic modes of action and anthelmintic resistance. Basic scientific, human and veterinary interests were addressed in oral and poster presentations. The delegates were from universities and industries in the US, Europe, Australia and New Zealand. The papers were a great representation of the field, and included the use of C. elegans for lead discovery, mechanisms of anthelmintic resistance, nematode neuropeptides, proteases, B. thuringiensis crystal protein, nicotinic receptors, emodepside, benzimidazoles, P-glycoproteins, natural products, microfluidic techniques and bioinformatics approaches. The NIH also presented NIAID-specific parasite genomic priorities and initiatives. From these papers we introduce below selected papers with a focus on anthelmintic drug screening and development.

  12. Usability of Discovery Portals

    OpenAIRE

    Bulens, J.D.; Vullings, L.A.E.; Houtkamp, J.M.; Vanmeulebrouk, B.

    2013-01-01

    As INSPIRE progresses to be implemented in the EU, many new discovery portals are built to facilitate finding spatial data. Currently the structure of the discovery portals is determined by the way spatial data experts like to work. However, we argue that the main target group for discovery portals are not spatial data experts but professionals with limited spatial knowledge, and a focus outside the spatial domain. An exploratory usability experiment was carried out in which three discovery p...

  13. Efficient approach for reliability-based optimization based on weighted importance sampling approach

    International Nuclear Information System (INIS)

    Yuan, Xiukai; Lu, Zhenzhou

    2014-01-01

    An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the ‘failure probability function (FPF)’. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology

  14. Pharmacologically directed strategies in academic anticancer drug discovery based on the European NCI compounds initiative.

    Science.gov (United States)

    Hendriks, Hans R; Govaerts, Anne-Sophie; Fichtner, Iduna; Burtles, Sally; Westwell, Andrew D; Peters, Godefridus J

    2017-07-11

    The European NCI compounds programme, a joint initiative of the EORTC Research Branch, Cancer Research Campaign and the US National Cancer Institute, was initiated in 1993. The objective was to help the NCI in reducing the backlog of in vivo testing of potential anticancer compounds, synthesised in Europe that emerged from the NCI in vitro 60-cell screen. Over a period of more than twenty years the EORTC-Cancer Research Campaign panel reviewed ∼2000 compounds of which 95 were selected for further evaluation. Selected compounds were stepwise developed with clear go/no go decision points using a pharmacologically directed programme. This approach eliminated quickly compounds with unsuitable pharmacological properties. A few compounds went into Phase I clinical evaluation. The lessons learned and many of the principles outlined in the paper can easily be applied to current and future drug discovery and development programmes. Changes in the review panel, restrictions regarding numbers and types of compounds tested in the NCI in vitro screen and the appearance of targeted agents led to the discontinuation of the European NCI programme in 2017 and its transformation into an academic platform of excellence for anticancer drug discovery and development within the EORTC-PAMM group. This group remains open for advice and collaboration with interested parties in the field of cancer pharmacology.

  15. A Sensitive Assay for Virus Discovery in Respiratory Clinical Samples

    NARCIS (Netherlands)

    de Vries, Michel; Deijs, Martin; Canuti, Marta; van Schaik, Barbera D. C.; Faria, Nuno R.; van de Garde, Martijn D. B.; Jachimowski, Loes C. M.; Jebbink, Maarten F.; Jakobs, Marja; Luyf, Angela C. M.; Coenjaerts, Frank E. J.; Claas, Eric C. J.; Molenkamp, Richard; Koekkoek, Sylvie M.; Lammens, Christine; Leus, Frank; Goossens, Herman; Ieven, Margareta; Baas, Frank; van der Hoek, Lia

    2011-01-01

    In 5-40% of respiratory infections in children, the diagnostics remain negative, suggesting that the patients might be infected with a yet unknown pathogen. Virus discovery cDNA-AFLP (VIDISCA) is a virus discovery method based on recognition of restriction enzyme cleavage sites, ligation of adaptors

  16. The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

    Science.gov (United States)

    Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

    2010-01-01

    The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

  17. The Implementation of Discovery Learning Method to Increase Learning Outcomes and Motivation of Student in Senior High School

    Directory of Open Access Journals (Sweden)

    Nanda Saridewi

    2017-11-01

    Full Text Available Based on data from the observation of high school students grade XI that daily low student test scores due to a lack of role of students in the learning process. This classroom action research aims to improve learning outcomes and student motivation through discovery learning method in colloidal material. This study uses the approach developed by Lewin consisting of planning, action, observation, and reflection. Data collection techniques used the questionnaires and ability tests end. Based on the research that results for students received a positive influence on learning by discovery learning model by increasing the average value of 74 students from the first cycle to 90.3 in the second cycle and increased student motivation in the form of two statements based competence (KD categories (sometimes on the first cycle and the first statement KD category in the second cycle. Thus the results of this study can be used to improve learning outcomes and student motivation

  18. 19 CFR 354.10 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery. 354.10 Section 354.10 Customs Duties... ANTIDUMPING OR COUNTERVAILING DUTY ADMINISTRATIVE PROTECTIVE ORDER § 354.10 Discovery. (a) Voluntary discovery. All parties are encouraged to engage in voluntary discovery procedures regarding any matter, not...

  19. 36 CFR 1150.63 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Discovery. 1150.63 Section... PRACTICE AND PROCEDURES FOR COMPLIANCE HEARINGS Prehearing Conferences and Discovery § 1150.63 Discovery. (a) Parties are encouraged to engage in voluntary discovery procedures. For good cause shown under...

  20. 37 CFR 11.52 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Discovery. 11.52 Section 11... Disciplinary Proceedings; Jurisdiction, Sanctions, Investigations, and Proceedings § 11.52 Discovery. Discovery... establishes that discovery is reasonable and relevant, the hearing officer, under such conditions as he or she...

  1. Pattern Discovery in Time-Ordered Data; TOPICAL

    International Nuclear Information System (INIS)

    CONRAD, GREGORY N.; BRITANIK, JOHN M.; DELAND, SHARON M.; JENKIN, CHRISTINA L.

    2002-01-01

    This report describes the results of a Laboratory-Directed Research and Development project on techniques for pattern discovery in discrete event time series data. In this project, we explored two different aspects of the pattern matching/discovery problem. The first aspect studied was the use of Dynamic Time Warping for pattern matching in continuous data. In essence, DTW is a technique for aligning time series along the time axis to optimize the similarity measure. The second aspect studied was techniques for discovering patterns in discrete event data. We developed a pattern discovery tool based on adaptations of the A-priori and GSP (Generalized Sequential Pattern mining) algorithms. We then used the tool on three different application areas-unattended monitoring system data from a storage magazine, computer network intrusion detection, and analysis of robot training data

  2. Therapeutics discovery: From bench to first in-human trials.

    Science.gov (United States)

    Al-Hujaily, Ensaf M; Khatlani, Tanvir; Alehaideb, Zeyad; Ali, Rizwan; Almuzaini, Bader; Alrfaei, Bahauddeen M; Iqbal, Jahangir; Islam, Imadul; Malik, Shuja; Marwani, Bader A; Massadeh, Salam; Nehdi, Atef; Alsomaie, Barrak; Debasi, Bader; Bushnak, Ibraheem; Noibi, Saeed; Hussain, Syed; Wajid, Wahid Abdul; Armand, Jean-Pierre; Gul, Sheraz; Oyarzabal, Julen; Rais, Rana; Bountra, Chas; Alaskar, Ahmed; Knawy, Bander Al; Boudjelal, Mohamed

    2018-03-01

    The 'Therapeutics discovery: From bench to first in-human trials' conference, held at the King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs (MNGHA), Kingdom of Saudi Arabia (KSA) from October 10-12, 2017, provided a unique opportunity for experts worldwide to discuss advances in drug discovery and development, focusing on phase I clinical trials. It was the first event of its kind to be hosted at the new research center, which was constructed to boost drug discovery and development in the KSA in collaboration with institutions, such as the Academic Drug Discovery Consortium in the United States of America (USA), Structural Genomics Consortium of the University of Oxford in the United Kingdom (UK), and Institute of Materia Medica of the Chinese Academy of Medical Sciences in China. The program was divided into two parts. A pre-symposium day took place on October 10, during which courses were conducted on clinical trials, preclinical drug discovery, molecular biology and nanofiber research. The attendees had the opportunity for one-to-one meetings with international experts to exchange information and foster collaborations. In the second part of the conference, which took place on October 11 and 12, the clinical trials pipeline, design and recruitment of volunteers, and economic impact of clinical trials were discussed. The Saudi Food and Drug Administration presented the regulations governing clinical trials in the KSA. The process of preclinical drug discovery from small molecules, cellular and immunologic therapies, and approaches to identifying new targets were also presented. The recommendation of the conference was that researchers in the KSA must invest more fund, talents and infrastructure to lead the region in phase I clinical trials and preclinical drug discovery. Diseases affecting the local population, such as Middle East Respiratory Syndrome and resistant bacterial infections, represent the optimal

  3. Usability of Discovery Portals

    NARCIS (Netherlands)

    Bulens, J.D.; Vullings, L.A.E.; Houtkamp, J.M.; Vanmeulebrouk, B.

    2013-01-01

    As INSPIRE progresses to be implemented in the EU, many new discovery portals are built to facilitate finding spatial data. Currently the structure of the discovery portals is determined by the way spatial data experts like to work. However, we argue that the main target group for discovery portals

  4. Automated Sample Preparation Platform for Mass Spectrometry-Based Plasma Proteomics and Biomarker Discovery

    Directory of Open Access Journals (Sweden)

    Vilém Guryča

    2014-03-01

    Full Text Available The identification of novel biomarkers from human plasma remains a critical need in order to develop and monitor drug therapies for nearly all disease areas. The discovery of novel plasma biomarkers is, however, significantly hampered by the complexity and dynamic range of proteins within plasma, as well as the inherent variability in composition from patient to patient. In addition, it is widely accepted that most soluble plasma biomarkers for diseases such as cancer will be represented by tissue leakage products, circulating in plasma at low levels. It is therefore necessary to find approaches with the prerequisite level of sensitivity in such a complex biological matrix. Strategies for fractionating the plasma proteome have been suggested, but improvements in sensitivity are often negated by the resultant process variability. Here we describe an approach using multidimensional chromatography and on-line protein derivatization, which allows for higher sensitivity, whilst minimizing the process variability. In order to evaluate this automated process fully, we demonstrate three levels of processing and compare sensitivity, throughput and reproducibility. We demonstrate that high sensitivity analysis of the human plasma proteome is possible down to the low ng/mL or even high pg/mL level with a high degree of technical reproducibility.

  5. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

    Science.gov (United States)

    Basith, Shaherin; Cui, Minghua; Macalino, Stephani J. Y.; Park, Jongmi; Clavio, Nina A. B.; Kang, Soosung; Choi, Sun

    2018-01-01

    The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the “golden age for GPCR structural biology.” Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand– and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed. PMID:29593527

  6. 14 CFR 16.213 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Discovery. 16.213 Section 16.213... PRACTICE FOR FEDERALLY-ASSISTED AIRPORT ENFORCEMENT PROCEEDINGS Hearings § 16.213 Discovery. (a) Discovery... discovery permitted by this section if a party shows that— (1) The information requested is cumulative or...

  7. 28 CFR 76.21 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Discovery. 76.21 Section 76.21 Judicial... POSSESSION OF CERTAIN CONTROLLED SUBSTANCES § 76.21 Discovery. (a) Scope. Discovery under this part covers... as a general guide for discovery practices in proceedings before the Judge. However, unless otherwise...

  8. Mass spectrometry for protein quantification in biomarker discovery.

    Science.gov (United States)

    Wang, Mu; You, Jinsam

    2012-01-01

    Major technological advances have made proteomics an extremely active field for biomarker discovery in recent years due primarily to the development of newer mass spectrometric technologies and the explosion in genomic and protein bioinformatics. This leads to an increased emphasis on larger scale, faster, and more efficient methods for detecting protein biomarkers in human tissues, cells, and biofluids. Most current proteomic methodologies for biomarker discovery, however, are not highly automated and are generally labor-intensive and expensive. More automation and improved software programs capable of handling a large amount of data are essential to reduce the cost of discovery and to increase throughput. In this chapter, we discuss and describe mass spectrometry-based proteomic methods for quantitative protein analysis.

  9. Metabolomics for Biomarker Discovery: Moving to the Clinic

    Science.gov (United States)

    Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

    2015-01-01

    To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases. PMID:26090402

  10. Reuse-oriented common structure discovery in assembly models

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Pan; Zhang Jie; Li, Yuan; Yu, Jian Feng [The Ministry of Education Key Lab of Contemporary Design and Integrated Manufacturing Technology, Northwestern Polytechnical University, Xian (China)

    2017-01-15

    Discovering the common structures in assembly models provides designers with the commonalities that carry significant design knowledge across multiple products, which helps to improve design efficiency and accelerate the design process. In this paper, a discovery method has been developed to obtain the common structure in assembly models. First, this work proposes a graph descriptor that captures both the geometrical and topological information of the assembly model, in which shape vectors and link vectors quantitatively describe the part models and mating relationships, respectively. Then, a clustering step is introduced into the discovery, which clusters the similar parts by comparing the similarities between them. In addition, some rules are also provided to filter the frequent subgraphs in order to obtain the expected results. Compared with the existing method, the proposed approach could overcome the disadvantages by providing an independent description of the part model and taking into consideration the similar parts in assemblies, which leads to a more reasonable result. Finally, some experiments have been carried out and the experimental results demonstrate the effectiveness of the proposed approach.

  11. Reuse-oriented common structure discovery in assembly models

    International Nuclear Information System (INIS)

    Wang, Pan; Zhang Jie; Li, Yuan; Yu, Jian Feng

    2017-01-01

    Discovering the common structures in assembly models provides designers with the commonalities that carry significant design knowledge across multiple products, which helps to improve design efficiency and accelerate the design process. In this paper, a discovery method has been developed to obtain the common structure in assembly models. First, this work proposes a graph descriptor that captures both the geometrical and topological information of the assembly model, in which shape vectors and link vectors quantitatively describe the part models and mating relationships, respectively. Then, a clustering step is introduced into the discovery, which clusters the similar parts by comparing the similarities between them. In addition, some rules are also provided to filter the frequent subgraphs in order to obtain the expected results. Compared with the existing method, the proposed approach could overcome the disadvantages by providing an independent description of the part model and taking into consideration the similar parts in assemblies, which leads to a more reasonable result. Finally, some experiments have been carried out and the experimental results demonstrate the effectiveness of the proposed approach

  12. Web-based services for drug design and discovery.

    Science.gov (United States)

    Frey, Jeremy G; Bird, Colin L

    2011-09-01

    Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.

  13. 40 CFR 27.21 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Discovery. 27.21 Section 27.21... Discovery. (a) The following types of discovery are authorized: (1) Requests for production of documents for..., discovery is available only as ordered by the presiding officer. The presiding officer shall regulate the...

  14. 37 CFR 41.150 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Discovery. 41.150 Section 41... COMMERCE PRACTICE BEFORE THE BOARD OF PATENT APPEALS AND INTERFERENCES Contested Cases § 41.150 Discovery. (a) Limited discovery. A party is not entitled to discovery except as authorized in this subpart. The...

  15. 14 CFR 13.220 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Discovery. 13.220 Section 13.220... INVESTIGATIVE AND ENFORCEMENT PROCEDURES Rules of Practice in FAA Civil Penalty Actions § 13.220 Discovery. (a) Initiation of discovery. Any party may initiate discovery described in this section, without the consent or...

  16. 49 CFR 604.38 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 49 Transportation 7 2010-10-01 2010-10-01 false Discovery. 604.38 Section 604.38 Transportation... TRANSPORTATION CHARTER SERVICE Hearings. § 604.38 Discovery. (a) Permissible forms of discovery shall be within the discretion of the PO. (b) The PO shall limit the frequency and extent of discovery permitted by...

  17. 15 CFR 719.10 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Discovery. 719.10 Section 719.10... Discovery. (a) General. The parties are encouraged to engage in voluntary discovery regarding any matter... the Federal Rules of Civil Procedure relating to discovery apply to the extent consistent with this...

  18. 24 CFR 26.18 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Discovery. 26.18 Section 26.18... PROCEDURES Hearings Before Hearing Officers Discovery § 26.18 Discovery. (a) General. The parties are encouraged to engage in voluntary discovery procedures, which may commence at any time after an answer has...

  19. 42 CFR 426.532 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 3 2010-10-01 2010-10-01 false Discovery. 426.532 Section 426.532 Public Health... § 426.532 Discovery. (a) General rule. If the Board orders discovery, the Board must establish a reasonable timeframe for discovery. (b) Protective order—(1) Request for a protective order. Any party...

  20. 49 CFR 1503.633 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 49 Transportation 9 2010-10-01 2010-10-01 false Discovery. 1503.633 Section 1503.633... Rules of Practice in TSA Civil Penalty Actions § 1503.633 Discovery. (a) Initiation of discovery. Any party may initiate discovery described in this section, without the consent or approval of the ALJ, at...

  1. 14 CFR 1264.120 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Discovery. 1264.120 Section 1264.120... PENALTIES ACT OF 1986 § 1264.120 Discovery. (a) The following types of discovery are authorized: (1..., discovery is available only as ordered by the presiding officer. The presiding officer shall regulate the...

  2. 22 CFR 128.6 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Discovery. 128.6 Section 128.6 Foreign... Discovery. (a) Discovery by the respondent. The respondent, through the Administrative Law Judge, may... discovery if the interests of national security or foreign policy so require, or if necessary to comply with...

  3. 24 CFR 26.42 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Discovery. 26.42 Section 26.42... PROCEDURES Hearings Pursuant to the Administrative Procedure Act Discovery § 26.42 Discovery. (a) General. The parties are encouraged to engage in voluntary discovery procedures, which may commence at any time...

  4. 49 CFR 386.37 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 49 Transportation 5 2010-10-01 2010-10-01 false Discovery. 386.37 Section 386.37 Transportation... and Hearings § 386.37 Discovery. (a) Parties may obtain discovery by one or more of the following...; and requests for admission. (b) Discovery may not commence until the matter is pending before the...

  5. 29 CFR 1955.32 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 9 2010-07-01 2010-07-01 false Discovery. 1955.32 Section 1955.32 Labor Regulations...) PROCEDURES FOR WITHDRAWAL OF APPROVAL OF STATE PLANS Preliminary Conference and Discovery § 1955.32 Discovery... allow discovery by any other appropriate procedure, such as by interrogatories upon a party or request...

  6. Astroparticle physics: puzzles and discoveries

    International Nuclear Information System (INIS)

    Berezinsky, V

    2008-01-01

    Puzzles often give birth to the great discoveries, the false discoveries sometimes stimulate the exiting ideas in theoretical physics. The historical examples of both are described in Introduction and in section 'Cosmological Puzzles'. From existing puzzles most attention is given to Ultra High Energy Cosmic Ray (UHECR) puzzle and to cosmological constant problem. The 40-years old UHECR problem consisted in absence of the sharp steepening in spectrum of extragalactic cosmic rays caused by interaction with CMB radiation. This steepening is known as Greisen-Zatsepin-Kuzmin (GZK) cutoff. It is demonstrated here that the features of interaction of cosmic ray protons with CMB are seen now in the spectrum in the form of the dip and beginning of the GZK cutoff. The most serious cosmological problem is caused by large vacuum energy of the known elementary-particle fields which exceeds at least by 45 orders of magnitude the cosmological vacuum energy. The various ideas put forward to solve this problem during last 40 years, have weaknesses and cannot be accepted as the final solution of this puzzle. The anthropic approach is discussed

  7. Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms.

    Science.gov (United States)

    Xu, Min; Chai, Xiaoqi; Muthakana, Hariank; Liang, Xiaodan; Yang, Ge; Zeev-Ben-Mordehai, Tzviya; Xing, Eric P

    2017-07-15

    Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data. To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. Source code freely available at http://www.cs.cmu.edu/∼mxu1/software . mxu1@cs.cmu.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  8. Motivating Communities To Go Beyond the Discovery Plateau

    Science.gov (United States)

    Habermann, T.; Kozimor, J.

    2014-12-01

    Years of emphasizing discovery and minimal metadata requirements have resulted in a culture that accepts that metadata are for discovery and complete metadata are too complex or difficult for researchers to understand and create. Evolving the culture past this "data-discovery plateau" requires a multi-faceted approach that addresses the rational and emotional sides of the problem. On the rational side, scientists know that data and results must be well documented in order to be reproducible, re-usable, and trustworthy. We need tools that script critical moves towards well-described destinations and help identify members of the community that are already leading the way towards those destinations. We need mechanisms that help those leaders share their experiences and examples. On the emotional side, we need to emphasize that high-quality metadata makes data trustworthy, divide the improvement process into digestible pieces and create mechanisms for clearly identifying and rewarding progress. We also need to provide clear opportunities for community members to increase their expertise and to share their skills.

  9. Targeting cysteine proteases in trypanosomatid disease drug discovery.

    Science.gov (United States)

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2017-12-01

    Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.

  10. Organic synthesis provides opportunities to transform drug discovery

    Science.gov (United States)

    Blakemore, David C.; Castro, Luis; Churcher, Ian; Rees, David C.; Thomas, Andrew W.; Wilson, David M.; Wood, Anthony

    2018-03-01

    Despite decades of ground-breaking research in academia, organic synthesis is still a rate-limiting factor in drug-discovery projects. Here we present some current challenges in synthetic organic chemistry from the perspective of the pharmaceutical industry and highlight problematic steps that, if overcome, would find extensive application in the discovery of transformational medicines. Significant synthesis challenges arise from the fact that drug molecules typically contain amines and N-heterocycles, as well as unprotected polar groups. There is also a need for new reactions that enable non-traditional disconnections, more C-H bond activation and late-stage functionalization, as well as stereoselectively substituted aliphatic heterocyclic ring synthesis, C-X or C-C bond formation. We also emphasize that syntheses compatible with biomacromolecules will find increasing use, while new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process. We believe that increasing collaboration between academic and industrial chemists is crucial to address the challenges outlined here.

  11. Computational discovery of extremal microstructure families

    Science.gov (United States)

    Chen, Desai; Skouras, Mélina; Zhu, Bo; Matusik, Wojciech

    2018-01-01

    Modern fabrication techniques, such as additive manufacturing, can be used to create materials with complex custom internal structures. These engineered materials exhibit a much broader range of bulk properties than their base materials and are typically referred to as metamaterials or microstructures. Although metamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand. We propose a computational approach to discover families of microstructures with extremal macroscale properties automatically. Using efficient simulation and sampling techniques, we compute the space of mechanical properties covered by physically realizable microstructures. Our system then clusters microstructures with common topologies into families. Parameterized templates are eventually extracted from families to generate new microstructure designs. We demonstrate these capabilities on the computational design of mechanical metamaterials and present five auxetic microstructure families with extremal elastic material properties. Our study opens the way for the completely automated discovery of extremal microstructures across multiple domains of physics, including applications reliant on thermal, electrical, and magnetic properties. PMID:29376124

  12. 42 CFR 426.432 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 3 2010-10-01 2010-10-01 false Discovery. 426.432 Section 426.432 Public Health... § 426.432 Discovery. (a) General rule. If the ALJ orders discovery, the ALJ must establish a reasonable timeframe for discovery. (b) Protective order—(1) Request for a protective order. Any party receiving a...

  13. 10 CFR 13.21 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 1 2010-01-01 2010-01-01 false Discovery. 13.21 Section 13.21 Energy NUCLEAR REGULATORY COMMISSION PROGRAM FRAUD CIVIL REMEDIES § 13.21 Discovery. (a) The following types of discovery are...) Unless mutually agreed to by the parties, discovery is available only as ordered by the ALJ. The ALJ...

  14. 49 CFR 1121.2 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 49 Transportation 8 2010-10-01 2010-10-01 false Discovery. 1121.2 Section 1121.2 Transportation... TRANSPORTATION RULES OF PRACTICE RAIL EXEMPTION PROCEDURES § 1121.2 Discovery. Discovery shall follow the procedures set forth at 49 CFR part 1114, subpart B. Discovery may begin upon the filing of the petition for...

  15. 38 CFR 42.21 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Discovery. 42.21 Section... IMPLEMENTING THE PROGRAM FRAUD CIVIL REMEDIES ACT § 42.21 Discovery. (a) The following types of discovery are... creation of a document. (c) Unless mutually agreed to by the parties, discovery is available only as...

  16. 22 CFR 521.21 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Discovery. 521.21 Section 521.21 Foreign... Discovery. (a) The following types of discovery are authorized: (1) Requests for production of documents for... interpreted to require the creation of a document. (c) Unless mutually agreed to by the parties, discovery is...

  17. 31 CFR 10.71 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Discovery. 10.71 Section 10.71 Money... SERVICE Rules Applicable to Disciplinary Proceedings § 10.71 Discovery. (a) In general. Discovery may be... relevance, materiality and reasonableness of the requested discovery and subject to the requirements of § 10...

  18. 39 CFR 955.15 - Discovery.

    Science.gov (United States)

    2010-07-01

    ... 39 Postal Service 1 2010-07-01 2010-07-01 false Discovery. 955.15 Section 955.15 Postal Service... APPEALS § 955.15 Discovery. (a) The parties are encouraged to engage in voluntary discovery procedures. In connection with any deposition or other discovery procedure, the Board may issue any order which justice...

  19. 43 CFR 35.21 - Discovery.

    Science.gov (United States)

    2010-10-01

    ... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Discovery. 35.21 Section 35.21 Public... AND STATEMENTS § 35.21 Discovery. (a) The following types of discovery are authorized: (1) Requests...) Unless mutually agreed to by the parties, discovery is available only as ordered by the ALJ. The ALJ...

  20. 15 CFR 766.9 - Discovery.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Discovery. 766.9 Section 766.9... PROCEEDINGS § 766.9 Discovery. (a) General. The parties are encouraged to engage in voluntary discovery... provisions of the Federal Rules of Civil Procedure relating to discovery apply to the extent consistent with...