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- Title
Computational drug repositioning using low-rank matrix approximation and randomized algorithms.
- Authors
Luo, Huimin; Li, Min; Wang, Shaokai; Liu, Quan; Li, Yaohang; Wang, Jianxin
- Abstract
Motivation: Computational drug repositioning is an important and efficient approach towards identifying novel treatments for diseases in drug discovery. The emergence of large-scale, heterogeneous biological and biomedical datasets has provided an unprecedented opportunity for developing computational drug repositioning methods. The drug repositioning problem can be modeled as a recommendation system that recommends novel treatments based on known drug-disease associations. The formulation under this recommendation system is matrix completion, assuming that the hidden factors contributing to drug-disease associations are highly correlated and thus the corresponding data matrix is low-rank. Under this assumption, the matrix completion algorithm fills out the unknown entries in the drug-disease matrix by constructing a low-rank matrix approximation, where new drug-disease associations having not been validated can be screened. Results: In this work, we propose a drug repositioning recommendation system (DRRS) to predict novel drug indications by integrating related data sources and validated information of drugs and diseases. Firstly, we construct a heterogeneous drug-disease interaction network by integrating drug-drug, disease-disease and drug-disease networks. The heterogeneous network is represented by a large drug-disease adjacency matrix, whose entries include drug pairs, disease pairs, known drug-disease interaction pairs and unknown drug-disease pairs. Then, we adopt a fast Singular Value Thresholding (SVT) algorithm to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. The comprehensive experimental results show that DRRS improves the prediction accuracy compared with the other state-of-the-art approaches. In addition, case studies for several selected drugs further demonstrate the practical usefulness of the proposed method.
- Subjects
MARKET repositioning; DRUG development; BIOMEDICAL engineering; THRESHOLDING algorithms; UNITED States. Food &; Drug Administration; SUPPORT vector machines
- Publication
Bioinformatics, 2018, Vol 34, Issue 11, p1904
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/bty013