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Article

Sequential regulatory activity prediction across chromosomes with convolutional neural networks.

Genome research - 1 May 2018

Kelley David R, Reshef Yakir A, Bileschi Maxwell, Belanger David, McLean Cory Y, Snoek Jasper

Abstract excerpt

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...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Share to Regulatory Sequences, Nucleic Acid

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Identifiers

PMID: 29588361
DOI: 10.1101/gr.227819.117
PMCID: PMC5932613

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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