RT Journal
A1 Kelley, David R.
A1 Reshef, Yakir A.
A1 Bileschi, Maxwell
A1 Belanger, David
A1 McLean, Cory Y.
A1 Snoek, Jasper
T1 Sequential regulatory activity prediction across chromosomes with convolutional neural networks
JF Genome Research 
JO Genome Research 
YR 2018 
FD May 01 
VO 28 
IS 5 
SP 739 
OP 750 
DO 10.1101/gr.227819.117 
UL http://genome.cshlp.org/content/28/5/739.abstract 
AB 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 show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable fine mapping of disease loci.