Heterogeneity Aware Random Forest for Drug Sensitivity Prediction.

ABSTRACT: Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogeneity Aware Ra

SUBMITTER: Rahman R 

PROVIDER: S-EPMC5595802 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature