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

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

Automated summary

Accurately predicting new drug interactions is crucial for preventing adverse reactions. A recent study highlights the importance of understanding non-linear effects of DDIs and introduces a new unsupervised learning approach. The analysis of gene expression data suggests that non-linear dose-dependence is not always a result of DDIs.