Representation learning of genomic sequence motifs with convolutional neural networks

Peter K Koo and Sean R Eddy

PLOS Computational Biology, 2019, vol. 15, issue 12, 1-17

Abstract: Although convolutional neural networks (CNNs) have been applied to a variety of computational genomics problems, there remains a large gap in our understanding of how they build representations of regulatory genomic sequences. Here we perform systematic experiments on synthetic sequences to reveal how CNN architecture, specifically convolutional filter size and max-pooling, influences the extent that sequence motif representations are learned by first layer filters. We find that CNNs designed to foster hierarchical representation learning of sequence motifs—assembling partial features into whole features in deeper layers—tend to learn distributed representations, i.e. partial motifs. On the other hand, CNNs that are designed to limit the ability to hierarchically build sequence motif representations in deeper layers tend to learn more interpretable localist representations, i.e. whole motifs. We then validate that this representation learning principle established from synthetic sequences generalizes to in vivo sequences.Author summary: Although deep convolutional neural networks (CNNs) have demonstrated promise across many regulatory genomics prediction tasks, their inner workings largely remain a mystery. Here we empirically demonstrate how CNN architecture influences the extent that representations of sequence motifs are captured by first layer filters. We find that max-pooling and convolutional filter size modulates information flow, controlling the extent that deeper layers can build features hierarchically. CNNs designed to foster hierarchical representation learning tend to capture partial representations of motifs in first layer filters. On the other hand, CNNs that are designed to limit the ability of deeper layers to hierarchically build upon low-level features tend to learn whole representations of motifs in first layer filters. Together, this study enables the design of CNNs that intentionally learn interpretable representations in easier to access first layer filters (with a small tradeoff in performance), versus building harder to interpret distributed representations, both of which have their strengths and limitations.

Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007560

DOI: 10.1371/journal.pcbi.1007560

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