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An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs
2018
Bioinformatics
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. Here we show how to
doi:10.1093/bioinformatics/bty964
pmid:30481258
pmcid:PMC6596897
fatcat:qbiuy4toqrgxvaxbxfq2sffhfu