Prediction of cancer dependencies from expression data using deep learning

Abstract

Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply them to suggest novel drug targets. Here we devise novel deep learning methods for predicting gene dependencies and drug sensitivities from gene expression measurements. By combining dimensionality reduction strategies, we are able to learn accurate models that outperform simpler neural networks or linear models.