DeepGREP: A deep convolutional neural network for predicting gene-regulating effects of small molecules

Abstract

Accurately predicting desired gene expression effects by using the representations of drugs and genes in silico is a key task in chemogenomics. This paper proposes DeepGREP, a deep learning model that can predict small molecules' gene regulation effects. The main motivation of this work is improving chemical-induced differential gene expression prediction by using a convolutional-based architecture to represent drugs and genes more effectively. To evaluate the performance of the DeepGREP, we conducted several experiments and compared them with DeepCop, the baseline model. The results show that DeepGREP outperforms the baseline model and significantly improves the gene expression prediction for AUC by around 4%, F-Score by around 15%, and Enrichment Factor by around 22%. We also demonstrate that the proposed method mostly outperforms the baseline in more difficulties setting of generalization to unseen molecules by using cold-drug splitting.