ArticleBMC bioinformatics2019
DeepPVP: phenotype-based prioritization of causative variants using deep learning.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- aiDIVA - hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models.NPJ genomic medicine · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce.Briefings in bioinformatics · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- Review
- Artificial Intelligence and Chromothripsis.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization.Genetics in medicine : official journal of the American College of Medical Genetics · 2025Article
- Pharmacogenomic landscape of TNF inhibitors in the Middle Eastern Qatari population.Frontiers in immunology · 2025Article
- Rare disease genomics and precision medicine.Genomics & informatics · 2024Review
- Exploring Aerobic Energy Metabolism in Breast Cancer: A Mutational Profile of Glycolysis and Oxidative Phosphorylation.International journal of molecular sciences · 2024Article
- Talking about diseases; developing a model of patient and public-prioritised disease phenotypes.NPJ digital medicine · 2024Article
- Clinical bioinformatics desiderata for molecular tumor boards.Briefings in bioinformatics · 2024Review
- Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.Bioinformatics (Oxford, England) · 2024Article
- Genomes in clinical care.NPJ genomic medicine · 2024Review
- Simulation of undiagnosed patients with novel genetic conditions.Nature communications · 2023Article
- Starvar: symptom-based tool for automatic ranking of variants using evidence from literature and genomes.BMC bioinformatics · 2023Article
- DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.PLoS computational biology · 2023Article
- IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined developmental disorders.HGG advances · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPrioritization 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.
resultsWe 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 .
conclusionsDeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.