DrPOCS: Drug Repositioning Based on Projection onto Convex Sets

Yin Ying Wang, Chunfeng Cui, Liqun Qi, Hong Yan, Xing Ming Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8350090
Pages (from-to)154-162
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume16
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Drug repositioning
  • matrix completion
  • projection onto convex sets (POCS)
  • singular value decomposition (SVD)

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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