Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 16, Issue 1, Pages 154-162Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2018.2830384
Keywords
Drug repositioning; projection onto convex sets (POCS); matrix completion; singular value decomposition (SVD)
Categories
Funding
- National Nature Science Foundation of China [61772368, 61572363, 91530321, 61602347]
- Natural Science Foundation of Shanghai [17ZR1445600]
- China Postdoctoral Science Foundation [2016M601647]
- City University of Hong Kong [7004862]
- Hong Kong Research Grants Council (RGC) of Hong Kong [C1007-15G]
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Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.
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