Computational Drug Repositioning Using Low-Rank Matrix Approximation and Randomized Algorithms
Document Type
Article
Publication Date
2018
DOI
10.1093/bioinformatics/bty013
Publication Title
Bioinformatics
Volume
34
Issue
11
Pages
1904-1912
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.
Rights
Copyright © 2025 Oxford University Press. All rights reserved.
Original Publication Citation
Luo, H., Li, M., Wang, S., Liu, Q., Li, Y., & Wang, J. (2018). Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics, 34(11), 1904-1912. https://doi.org/10.1093/bioinformatics/bty013
Repository Citation
Luo, H., Li, M., Wang, S., Liu, Q., Li, Y., & Wang, J. (2018). Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics, 34(11), 1904-1912. https://doi.org/10.1093/bioinformatics/bty013
