Novel Method for the Prediction of Drug-Drug Interactions Based on Gene Expression Profiles

The accurate prediction of new interactions between drugs is important for avoiding unknown(mild or severe) adverse reactions to drug combinations. The development of effective in silico methodsfor evaluating drug interactions based on gene expression data requires an understanding of how variousdrugs alter gene expression. Current computational methods for the prediction of drug-drug interactions(DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited inthe absence of known predictive DDIs. To improve DDIs’ interpretation, a recent study has demonstratedstrong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervisedlearning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D.We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. Wefound that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot alwaysbe attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.