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Kerrien S.,European Bioinformatics Institute | Aranda B.,European Bioinformatics Institute | Breuza L.,Swiss Institute of Bioinformatics | Bridge A.,Swiss Institute of Bioinformatics | And 18 more authors.
Nucleic Acids Research | Year: 2012

IntAct is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. Two levels of curation are now available within the database, with both IMEx-level annotation and less detailed MIMIx-compatible entries currently supported. As from September 2011, IntAct contains approximately 275 000 curated binary interaction evidences from over 5000 publications. The IntAct website has been improved to enhance the search process and in particular the graphical display of the results. New data download formats are also available, which will facilitate the inclusion of IntAct?s data in the Semantic Web. IntAct is an active contributor to the IMEx consortium (http://www .imexconsortium.org). IntAct source code and data are freely available at http://www.ebi.ac.uk/intact. © The Author(s) 2011. Published by Oxford University Press. Source


Gurulingappa H.,Molecular Connections Pvt Ltd | Mateen-Rajpu A.,Merck KGaA | Toldo L.,Merck KGaA
Journal of Biomedical Semantics | Year: 2012

The sheer amount of information about potential adverse drug events publishedin medical case reports pose major challenges for drug safety experts toperform timely monitoring. Efficient strategies for identification andextraction of information about potential adverse drug events fromfree-text resources are needed to support pharmacovigilance researchand pharmaceutical decision making. Therefore, this work focusses on theadaptation of a machine learning-based system for the identificationand extraction of potential adverse drug event relations from MEDLINE casereports. It relies on a high quality corpus that was manually annotatedusing an ontology-driven methodology. Qualitative evaluation of thesystem showed robust results. An experiment with large scale relationextraction from MEDLINE delivered under-identified potential adversedrug events not reported in drug monographs. Overall, this approach providesa scalable auto-assistance platform for drug safety professionals toautomatically collect potential adverse drug events communicated asfree-text data. © 2012 Gurulingappa et al.; licensee BioMed Central Ltd. Source


Gurulingappa H.,Molecular Connections Pvt Ltd | Toldo L.,Merck KGaA | Rajput A.M.,Merck KGaA | Rajput A.M.,University of Bonn | And 3 more authors.
Pharmacoepidemiology and Drug Safety | Year: 2013

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


Malhotra A.,Fraunhofer Institute for Algorithms and Scientific Computing | Malhotra A.,University of Bonn | Younesi E.,Fraunhofer Institute for Algorithms and Scientific Computing | Younesi E.,University of Bonn | And 4 more authors.
PLoS Computational Biology | Year: 2013

Speculative statements communicating experimental findings are frequently found in scientific articles, and their purpose is to provide an impetus for further investigations into the given topic. Automated recognition of speculative statements in scientific text has gained interest in recent years as systematic analysis of such statements could transform speculative thoughts into testable hypotheses. We describe here a pattern matching approach for the detection of speculative statements in scientific text that uses a dictionary of speculative patterns to classify sentences as hypothetical. To demonstrate the practical utility of our approach, we applied it to the domain of Alzheimer's disease and showed that our automated approach captures a wide spectrum of scientific speculations on Alzheimer's disease. Subsequent exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches, and can thus provide added value to ongoing research activities. © 2013 Malhotra et al. Source


Orchard S.,European Bioinformatics Institute | Ammari M.,University of Arizona | Aranda B.,European Bioinformatics Institute | Breuza L.,Swiss Institute of Bioinformatics | And 32 more authors.
Nucleic Acids Research | Year: 2014

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org). © 2013 The Author(s). Published by Oxford University Press. Source

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