RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

Background It often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results: show the strength of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.