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dc.contributor.author Chen, Bin
dc.contributor.author Ding, Ying
dc.contributor.author Wild, David J.
dc.date.accessioned 2013-01-16T15:22:00Z
dc.date.available 2013-01-16T15:22:00Z
dc.date.issued 2012-07
dc.identifier.citation Chen B, Ding Y, Wild DJ (2012) Assessing Drug Target Association Using Semantic Linked Data. PLoS Comput Biol 8(7): e1002574. doi:10.1371/ journal.pcbi.1002574 en_US
dc.identifier.uri http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002574 en_US
dc.identifier.uri http://hdl.handle.net/2022/15243
dc.description.abstract The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a 157|1683 score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap. en_US
dc.description.sponsorship BC and DJW were supported by an unrestricted grant from Pfizer Global Research and Development. YD is supported by NIH funded VIVO Project (UF09179). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. en_US
dc.language.iso en_US en_US
dc.publisher PLoS Computational Biology en_US
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. en_US
dc.title Assessing Drug Target Association Using Semantic Linked Data en_US
dc.type Article en_US


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