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Deciphering gene regulatory networks is a central problem in computationalbiology. Here, we explore the use of multi-modal neural networks to learnpredictive models of gene expression that include cis and trans regulatorycomponents. We learn models of stress response in the budding yeastSaccharomyces cerevisiae. Our models achieve high performance and substantiallyoutperform other state-of-the-art methods such as boosting algorithms that usepre-defined cis-regulatory features. Our model learns several cis and transregulators including well-known master stress response regulators. We use ourmodels to perform in-silico TF knock-out experiments and demonstrate thatin-silico predictions of target gene changes correlate with the results of thecorresponding TF knockout microarray experiment.

A multi-modal neural network for learning cis and trans regulation of stress response in yeast