A multivariate random forest based framework for drug sensitivity prediction

Drug sensitivity prediction based on genomic characterization remains a significant challenge in the area of systems medicine. Multiple approaches have been proposed for mapping genomic characterization to drug sensitivity and among them ensemble based learning techniques like Random Forests (RF) have been a top performer [1, 2]. The majority of the current approaches infer a predictive model for each drug individually but correlation between different drug sensitivities suggests that multiple response prediction incorporating the co-variance of the different drug responses can possibly improve prediction accuracy. In this abstract, we report a prediction and analysis framework based on Multivariate Random Forests (MRF) that incorporates the correlation between different drug sensitivities.