An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Jul 1, 2019
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.