Sequential regulatory activity prediction across chromosomes with convolutional neural networks.
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ABSTRACT:
SUBMITTER: Kelley DR
PROVIDER: S-EPMC5932613 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
Publications
Sequential regulatory activity prediction across chromosomes with convolutional neural networks.
Kelley David R DR Reshef Yakir A YA Bileschi Maxwell M Belanger David D McLean Cory Y CY Snoek Jasper J
Genome research 20180327 5
Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. By use of convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We sho ...[more]
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