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Deep learning of genomic variation and regulatory network data

Item Type:Review
Title:Deep learning of genomic variation and regulatory network data
Creators Name:Telenti, A. and Lippert, C. and Chang, P.C. and DePristo, M.
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 (eg. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome datasets, 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.
Keywords:Deep Learning, DNA Sequence Analysis, Exome, Gene Regulatory Networks, Genomics, Human Genome, High-Throughput Nucleotide Sequencing, Software
Source:Human Molecular Genetics
ISSN:0964-6906
Publisher:Oxford University Press (U.K.)
Volume:27
Number:R1
Page Range:R63-R71
Date:1 May 2018
Official Publication:https://doi.org/10.1093/hmg/ddy115
PubMed:View item in PubMed

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