MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values.

Authors

  • Mohamed Reda El Khili
    Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada.
  • Safyan Aman Memon
    Computational Biology Research Lab, National University of Computer and Emerging Sciences, Islamabad, Pakistan.
  • Amin Emad
    Department of Electrical and Computer Engineering, McGill University, Montréal, QC H3A 0G4, Canada.