Assessing drug target association using semantic linked data

• Published in: PLoS computational biology Vol. 8; no. 7; p. e1002574
• Main Authors: Chen, Bin, Ding, Ying, Wild, David J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-07-2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology; Computational biology; Computational Biology - methods; Computational chemistry; Data mining; Data Mining - methods; Databases, Factual; Disease; Drug Discovery - methods; Experiments; Gene expression; Humans; Life sciences; Linked Data; Medicine; Models, Theoretical; Ontology; Pharmaceutical chemistry; Pharmaceutical industry; Proteins; R&D; Reproducibility of Results; Research & development; Semantics; Social networks; Social research; United States; Models, Theoretical; Reproducibility of Results; Algorithms; Semantics; Humans; Computational Biology; Data Mining; Drug Discovery; Databases, Factual
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002574

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 [Formula: see text] 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.