Format

Send to

Choose Destination
Hum Mol Genet. 2018 May 1;27(R1):R63-R71. doi: 10.1093/hmg/ddy115.

Deep learning of genomic variation and regulatory network data.

Author information

1
Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA.
2
Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany.
3
Google Inc., Mountain View, CA 94043, USA.

Abstract

The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of population data and machine-learning strategies to solve complex genomic sequence regions. The review also portrays the rapid adoption of artificial intelligence/deep neural networks in genomics; in particular, deep learning approaches are well suited to model the complex dependencies in the regulatory landscape of the genome, and to provide predictors for genetic variant calling and interpretation.

PMID:
29648622
DOI:
10.1093/hmg/ddy115

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center