Quantum mechanics

Algorithm

Gaussian

Biology

Materials science

Theoretical computer science

Rank (graph theory)

Data mining

Computer science

Drug

Thresholding

Drug discovery

Matrix (chemical analysis)

Physics

Bioinformatics

Graph

Mathematics

Adjacency matrix

Image (mathematics)

Pharmacology

Artificial intelligence

Drug repositioning

Composite material

Combinatorics

Medicine

Matrix completion

Disease

Machine learning

Pathology

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.

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