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

Research output: Contribution to journalArticle

Standard

DeepPVP : phenotype-based prioritization of causative variants using deep learning. / Boudellioua, Imane; Kulmanov, Maxat; Schofield, Paul N.; Gkoutos, Georgios V.; Hoehndorf, Robert.

In: BMC Bioinformatics, Vol. 20, No. 1, 65, 06.02.2019.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Boudellioua, Imane ; Kulmanov, Maxat ; Schofield, Paul N. ; Gkoutos, Georgios V. ; Hoehndorf, Robert. / DeepPVP : phenotype-based prioritization of causative variants using deep learning. In: BMC Bioinformatics. 2019 ; Vol. 20, No. 1.

Bibtex

@article{19b1ee0e697e4beeb6ee4a118d4f1299,
title = "DeepPVP: phenotype-based prioritization of causative variants using deep learning",
abstract = "Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp. Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.",
keywords = "Machine learning, Ontology, Phenotype, Variant prioritization",
author = "Imane Boudellioua and Maxat Kulmanov and Schofield, {Paul N.} and Gkoutos, {Georgios V.} and Robert Hoehndorf",
year = "2019",
month = "2",
day = "6",
doi = "10.1186/s12859-019-2633-8",
language = "English",
volume = "20",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - DeepPVP

T2 - phenotype-based prioritization of causative variants using deep learning

AU - Boudellioua, Imane

AU - Kulmanov, Maxat

AU - Schofield, Paul N.

AU - Gkoutos, Georgios V.

AU - Hoehndorf, Robert

PY - 2019/2/6

Y1 - 2019/2/6

N2 - Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp. Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

AB - Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp. Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

KW - Machine learning

KW - Ontology

KW - Phenotype

KW - Variant prioritization

UR - http://www.scopus.com/inward/record.url?scp=85061131814&partnerID=8YFLogxK

U2 - 10.1186/s12859-019-2633-8

DO - 10.1186/s12859-019-2633-8

M3 - Article

C2 - 30727941

AN - SCOPUS:85061131814

VL - 20

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 1

M1 - 65

ER -