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motifNet: Deep learning for system identification of regulatory networks

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System identification of gene regulatory networks based on proteomics or transcriptomics time course data remains a hard problem, where even state-of-the-art algorithms perform relatively poorly in the regime of large system sizes. This work investigates whether machine learning methods, particularly deep learning, can exploit features of biological networks beyond first principles of chemical kinetics in order to improve performance on this task. We devised a deep neural network architecture called motifNet, combining convolutional and recurrent neural network elements, to tackle this problem. Our framework performs better than the state-of-the-art comparison methods in terms of area under the ROC and PR curve on unseen time course data simulated from the S. cerevisiae and E. coli gene regulatory network.

This talk is part of the Computational Neuroscience series.

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