title: Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction.
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 prediction 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.
authors.affiliation: Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.
authors.forename: Raziur
authors.initials: R
authors.lastname: Rahman
authors.affiliation: Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.
authors.forename: Saad
authors.initials: S
authors.lastname: Haider
authors.affiliation: Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, USA.
authors.forename: Souparno
authors.initials: S
authors.lastname: Ghosh
authors.affiliation: Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.
authors.forename: Ranadip
authors.initials: R
authors.lastname: Pal
keywords: drug sensitivity prediction; heteroscedasticity; probabilistic random forests; variance analysis of random forests