ArticleNature communications2021
MVP predicts the pathogenicity of missense variants by deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.
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Who cites it
107 citing papers in PubMed.
- EMMVEP: An Ensemble Method for Protein Missense Variant Effect Prediction Based on Multi-Source Feature Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- Pan-cancer prevalence of microsatellite instability and Lynch syndrome in India.Familial cancer · 2026Article
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- SIMLINK enables accurate variant pathogenicity prediction through modeling the gene-variant-feature association structure.Bioinformatics (Oxford, England) · 2026Article
- Unveiling New Insights: ReinterpretingClinical case reports · 2026Article
- Artificial Intelligence in Inherited Epidermolysis Bullosa: Current Evidence, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Assessing the contribution of rare variants to congenital heart disease through a large-scale case-control exome study.NPJ genomic medicine · 2026Article
- Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAEpilepsia open · 2026Review
- Bazedoxifene reverses sexually dimorphic autistic-like abnormalities in biallelic MDGA1-mutant mice.EMBO molecular medicine · 2026Article
- MissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scores.bioRxiv : the preprint server for biology · 2026Article
- Phenotype-first patient matching with SimPheny identifies diagnostic candidates beyond curated gene associations.medRxiv : the preprint server for health sciences · 2026Article
- SomaMutDB 2.0: A comprehensive database for exploring somatic mutations and their functional impact in normal human tissues.Nucleic acids research · 2026Article
- DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins.Frontiers in bioinformatics · 2026Article
- Reclassification of missense variant pathogenicity using ClinGen recommendations for recalibrated PP3/BP4 in silico predictor score thresholds.Genetics in medicine open · 2026Article
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Expanding the utility of variant effect predictions with phenotype-specific models.Nature communications · 2025Article
- An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser.Genome medicine · 2025Article
- Computational Analysis ofACS chemical neuroscience · 2025Article
- Meta-analysis reveals transcription factors and DNA binding domain variants associated with congenital heart defect and orofacial cleft.NPJ genomic medicine · 2025Article
- Comprehensive in Silico Reclassification of MECP2 Variants of Uncertain Significance in Rett Syndrome: Performance Evaluation and Structural Analysis.Journal of molecular neuroscience : MN · 2025Article
47 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. Here, we describe MVP (Missense Variant Pathogenicity prediction), a new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors. We train the model separately in genes that are intolerant of loss of function variants and the ones that are tolerant in order to take account of potentially different genetic effect size and mode of action. We compile cancer mutation hotspots and de novo variants from developmental disorders for benchmarking. Overall, MVP achieves better performance in prioritizing pathogenic missense variants than previous methods, especially in genes tolerant of loss of function variants. Finally, using MVP, we estimate that de novo coding variants contribute to 7.8% of isolated congenital heart disease, nearly doubling previous estimates.
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