DeepWAS scales full-likelihood training of flexible variant-effect priors to millions of variants and finds larger neural-network priors generalize better than smaller ones.
M., DenAdel, A., and Crawford, L
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Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra
DeepWAS scales full-likelihood training of flexible variant-effect priors to millions of variants and finds larger neural-network priors generalize better than smaller ones.