Name
Abstract | ||
|---|---|---|
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.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.An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1093/bioinformatics/btp433 | Bioinformatics |
Keywords | Field | DocType |
putative drug,definitive prediction,bipartite local model,target protein,new potential drug,target interaction,known drug,new drug,independent prediction,target interaction network,unknown drug,supervised prediction,computational biology,binding sites,drug targeting,algorithms,drug discovery,proteins | Data mining,Biological data,Drug discovery,Inference,Computer science,Bipartite graph,Drug target,Bioinformatics,In silico | Journal |
Volume | Issue | ISSN |
25 | 18 | 1367-4811 |
Citations | PageRank | References |
125 | 3.77 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bleakley, Kevin | 1 | 285 | 16.82 |
| Yoshihiro Yamanishi | 2 | 1268 | 83.44 |