The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.

ABSTRACT: Population-scale genomic data sets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information from genomic sequences to infer demographic events such as population size changes and gene flow among closely related populations/species, construct recombination maps, and uncover loci underlying recent adaptation. To date, most methods make use of only one or a few summaries of the input sequences and therefore ignore potentially useful information encoded in the data. The most sophisticated of these approaches involve likelihood calculations, which require theoretical advances for each new problem, and often focus on a single aspect of the da

SUBMITTER: Flagel L 

PROVIDER: S-EPMC6367976 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature