Computational drug repositioning using low-rank matrix approximation and randomized algorithms

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 com...

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Bibliographic Details
Published in:Bioinformatics Vol. 34; no. 11; pp. 1904 - 1912
Main Authors: Luo, Huimin, Li, Min, Wang, Shaokai, Liu, Quan, Li, Yaohang, Wang, Jianxin
Format: Journal Article
Language:English
Published: England Oxford University Press 01.06.2018
Subjects:
Algorithms
Computational Biology - methods
Drug Discovery - methods
Drug Repositioning - methods
Humans
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:Get full text
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Summary: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. Availability and implementation http://bioinformatics.csu.edu.cn/resources/softs/DrugRepositioning/DRRS/index.html Supplementary information Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bty013