Supervised prediction of drug-target interactions using bipartite local models

Kevin Bleakley, Yoshihiro Yamanishi

研究成果: Contribution to journalArticle査読

380 被引用数 (Scopus)

抄録

Motivation: In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug-target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug-target interactions. Results: We propose a novel supervised inference method to predict unknown drug-target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug-target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug-target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug-target interactions.

本文言語英語
ページ(範囲)2397-2403
ページ数7
ジャーナルBioinformatics
25
18
DOI
出版ステータス出版済み - 9 2009
外部発表はい

All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 計算理論と計算数学
  • 計算数学

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