DrPOCS : Drug repositioning based on projection onto convex sets

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

17 Scopus Citations
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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)154-162
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume16
Issue number1
Online published26 Apr 2018
Publication statusPublished - Jan 2019

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.

Research Area(s)

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

Citation Format(s)

DrPOCS: Drug repositioning based on projection onto convex sets. / Wang, Yin-Ying; Cui, Chunfeng; Qi, Liqun et al.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 1, 01.2019, p. 154-162.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review