Learning distance-dependent motif interactions: an interpretable CNN model of genomic events

Quinn, T. P.; Nguyen, D.; Nguyen, P.; Gupta, S.; Venkatesh, S.

doi:10.1101/2020.08.27.270967

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bioRxiv · 10.1101/2020.08.27.270967

Learning distance-dependent motif interactions: an interpretable CNN model of genomic events

Quinn, T. P.·Nguyen, D.·Nguyen, P.·Gupta, S.·Venkatesh, S.

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Abstract

In many biological studies, prediction is used primarily to validate the model; the real quest is to understand the underlying phenomenon. Therefore, interpretable deep models for biological studies are required. Here, we propose the Hyper-parameter eXplainable Motif Pair framework (HyperXPair) to model biological motifs and their distance-dependent context through explicitly interpretable parameters. This makes HyperXPair more than a decision-support tool; it is also a hypothesis-generating tool designed to advance knowledge in the field. We demonstrate the utility of our model by learning distance-dependent motif interactions for two biological problems: transcription initiation and RNA splicing.

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Quinn, T. P., Nguyen, D., Nguyen, P., Gupta, S., Venkatesh, S.. 2020-08-29. Learning distance-dependent motif interactions: an interpretable CNN model of genomic events. https://doi.org/10.1101/2020.08.27.270967

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