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

ABSTRACT:

Motivation

Convolutional neural networks (CNNs) have been tremendously successful in many contexts, particularly where training data are abundant and signal-to-noise ratios are large. However, when predicting noisily observed 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 process.

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 Monte Carlo dropout suggested by a reinterpretation of dropout as a variational Bayesian approximation, to develop a model that exhib

SUBMITTER: Brown RC 

PROVIDER: S-EPMC6596897 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature