Semi-supervised graph cut algorithm for drug repositioning by integrating drug, disease and genomic associations

Drug discovery is a cost expensive and time consuming process. Approved drugs have favorable or validated pharmacokinetic properties and toxicological profiles. Therefore repurposing approved drugs to new diseases can potentially avoid expensive costs associated with the early-stage testing. In this work, we propose to integrate drug-drug, disease-disease, gene-gene and drug-disease associations to reposition the approved drugs. Firstly, multiple sources of data are integrated into three layers: the chemical/phenotype layer, the gene/machanism layer and the treatment layer. Secondly, the drug-drug and disease-disease similarities in three layers are respectively computed and then combined together. Finally, based on the hypothesis that similar drugs may treat similar diseases, we model the drug repositioning problem as an optimal problem and propose a semi-supervised graph cut (SSGC) algorithm to solve the problem. The experimental results show that integrating multiple sources of data can achieve better performances than only considering single kind of data, and our method outperforms three representative approaches. Moreover, the predicted top-ranked repositioned relations have been reported in literature, illustrating the usefulness of our method in practice. The predicted results are available at https://github.com/wgs666/SSGC.git.