Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction

Authors

Raziur Rahman, Texas Tech University
Saad Haider, Texas Tech University
Souparno Gosh, Texas Tech University
Ranadip Pal, Texas Tech UniversityFollow

Date of this Version

2015

Citation

Rahman et al. Design of Probabilistic random forests with applications to anticancer Drug sensitivity Prediction. Cancer Informatics 2015:14(s5) 57–73 doi: 10.4137/CIn.s30794.

Comments

the authors, publisher and licensee Libertas academica Limited.

Abstract

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity predic- tion problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error.