Novel method for the prediction of drug-drug Interaction based on gene expression profiles.

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