DrPOCS : Drug repositioning based on projection onto convex sets
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 154-162 |
Journal / Publication | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 16 |
Issue number | 1 |
Online published | 26 Apr 2018 |
Publication status | Published - Jan 2019 |
Link(s)
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.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 1, 01.2019, p. 154-162.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review