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

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

BMC Bioinformatics 2019 article cited by 78

Source Open-access PDF More in Biology & Life Sciences

importance 6/10 ai assisted Genomics and Rare Diseases genomic diagnostics stage: research open-source model

Problem

Identifying the causative variant among thousands of candidates in a patient's whole exome or genome sequence is a major challenge.

Why it matters

Finding the causative variant enables genetic diagnosis, which guides patient care.

What the AI did

DeepPVP combined automated phenotype inference with deep neural networks to prioritize likely causative variants in exome/genome data.

Results

Stated to perform significantly better than existing methods; specific metrics not stated in source

Impact

Peer-reviewed tool offering improved computational ranking of candidate causative variants.

Record

Venue	BMC Bioinformatics
Published	2019
Work type	article
Authors	Imane Boudellioua, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos, Robert Hoehndorf
Citations	78 (OpenAlex, at import time)
References	36
Model or method	DeepPVP (deep neural networks)
Organization	not stated in source
Primary source	https://doi.org/10.1186/s12859-019-2633-8
Open access	gold
Keywords	variant prioritization, deep learning, exome sequencing, phenotype similarity, genetic diagnosis

Importance rating

6 / 10

significant — thousands to millions affected

Prioritizing causative variants among thousands of candidates in exome/genome data enables genetic diagnosis guiding patient care; abstract gives no scale or prevalence figures.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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