DeepPVP: phenotype-based prioritization of causative variants using deep learning-Reference-Cited by-同舟云学术

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DeepPVP: phenotype-based prioritization of causative variants using deep learning

Published:2019-02-06 Issue:1 Volume:20 Page:
ISSN:1471-2105
Container-title:BMC Bioinformatics
language:en
Short-container-title:BMC Bioinformatics

Author:

Boudellioua Imane,Kulmanov Maxat,Schofield Paul N.,Gkoutos Georgios V.,Hoehndorf Robert^ORCID

Funder

King Abdullah University of Science and Technology

Horizon 2020

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

Link

http://link.springer.com/content/pdf/10.1186/s12859-019-2633-8.pdf

Reference38 articles.

1. Eilbeck K, Quinlan A, Yandell M. Settling the score: variant prioritization and mendelian disease. Nat Rev Genet. 2017; 18(10):599–612. https://doi.org/10.1038/nrg.2017.52 .

2. Huang Y-FF, Gulko B, Siepel A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat Genet. 2017; 49(4):618–24.

3. Kircher M, Witten D, Jain P, O’Roak B, Cooper G, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014; 46(5):310–5. https://doi.org/10.1038/ng.2892 .

4. Quang D, Chen Y, Xie X. Dann: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015; 31(5):761–3.

5. MacArthur DG, Tyler-Smith C. Loss-of-function variants in the genomes of healthy humans. Hum Mol Genet. 2010; 19(R2):125–30.

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