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Review
. 2018 May 1;27(R1):R63-R71.
doi: 10.1093/hmg/ddy115.

Deep learning of genomic variation and regulatory network data

Amalio Telenti  1 , Christoph Lippert  2 , Pi-Chuan Chang  3 , Mark DePristo  3
Affiliations
Review

Deep learning of genomic variation and regulatory network data

Amalio Telenti et al. Hum Mol Genet. .

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.

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Figures

Figure 1.
Figure 1.
Convergence of data-rich technologies fueling the field of machine learning for genomics.
See this image and copyright information in PMC

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