RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

Fahimian, G.; Zahiri, J.; Arab, S. S.; Hassan-Sajedi, R.

doi:10.1101/817882

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bioRxiv · 10.1101/817882

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

Fahimian, G.·Zahiri, J.·Arab, S. S.·Hassan-Sajedi, R.

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Abstract

BackgroundIt 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.\n\nMethodsIn 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.\n\nResultsThe 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.\n\nConclusionResults 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.

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Fahimian, G., Zahiri, J., Arab, S. S., Hassan-Sajedi, R.. 2019-10-30. RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks. https://doi.org/10.1101/817882

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