An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs

By Richard C. Brown, Gerton A. Lunter. 2018

18 citations recorded by the source corpus.

Motivation Convolutional neural networks (CNNs) have been trememdously successful in many contexts, particularly where training data is abundant and signal-to-noise ratios are large. However, when predicting noisily observed biological phenotypes from DNA sequence, each training instance is only weakly informative, and the amount of training data is often fundamentally limited, emphasizing the need for methods that make optimal use of training data and any structure inherent in the model. Results Here we show how to combine equivariant networks, a general mathematical framework for handling exact symmetries in CNNs, with Bayesian dropout, a version of MC dropout suggested by a reinterpretation of dropout as a variational Bayesian approximation, to develop a model that exhibits exact reverse-complement symmetry and is more resistant to overtraining. We find that this model has increased power and generalizability, resulting in significantly better predictive accuracy compared to standard CNN implementations and state-of-art deep-learning-based motif finders. We use our network to predict recombination hotspots from sequence, and identify high-resolution binding motifs for the recombination-initiation protein PRDM9, which were recently validated by high-resolution assays. The network achieves a predictive accuracy comparable to that attainable by a direct assay of the H3K4me3 histone mark, a proxy for PRDM9 binding. Availability https://github.com/luntergroup/EquivariantNetworks Contact richard.brown@well.ox.ac.uk, gerton.lunter@well.ox.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019, background): “One such rational design principle that has met with substantial empirical success (Winkels & Cohen, 2018; Za-heer et al., 2017; Lunter & Brown, 2018) maintains that network architectures should be equivariant to symmetry transformations.”
Deep Scale-spaces: Equivariance Over Scale (2019, unknown): “…et al., 2018b, Bekkers et al., 2018, Hoogeboom et al., 2018] and 3D [Worrall et al., 2017], continuous rotations on the sphere [Esteves et al., 2018, Cohen et al., 2018b], in-plane reﬂections [Cohen and Welling, 2016a], and even reverse-conjugate symmetry in DNA sequencing [Lunter and Brown, 2018].”