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An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs
[article]
2018
bioRxiv
pre-print
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.
doi:10.1101/351254
fatcat:xm2ic6zbjfctrmzzril4uku25q