ArticleDisease models & mechanisms2024
Making sense of missense: challenges and opportunities in variant pathogenicity prediction.
Article in Disease models & mechanisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Probabilistic mapping of sub-genic intolerance reveals functional and disease-critical protein regions.bioRxiv : the preprint server for biology · 2026Article
- Deep phenotyping of EHMT1 ankyrin repeat domain missense variants in Kleefstra syndrome by multi-tiered structural genomics analyses at atomic resolution.Human molecular genetics · 2026Article
- Allele frequencies at recessive disease genes are mainly determined by pleiotropic effects in heterozygotes.Genetics · 2026Article
- Before new variants in genes associated with hematuria or proteinuria can be classified as causative, their pathogenicity must be demonstrated.Kidney research and clinical practice · 2026Article
- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- Comprehensive Insights into Perrault Syndrome: Genetic Diversity and Clinical Implications.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Benchmarking genetic birth prevalence estimates against newborn screening data.American journal of human genetics · 2026Article
- Rare coding variant architecture and gene discovery from 130,000 sequenced cases of atrial fibrillation.Research square · 2026Article
- MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Scientific reports · 2026Article
- Structural Genomics Defines PBRM1 Bromodomain Variant Function in ccRCC.Human mutation · 2026Article
- Know your scientist: KYC as biosecurity infrastructure.Frontiers in microbiology · 2026Article
- Allele Frequencies at Recessive Disease Genes are Mainly Determined by Pleiotropic Effects in Heterozygotes.bioRxiv : the preprint server for biology · 2025Article
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3 authors.
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Abstract
Computational tools for predicting variant pathogenicity are widely used to support clinical variant interpretation. Recently, several models, which do not rely on known variant classifications during training, have been developed. These approaches can potentially overcome biases of current clinical databases, such as misclassifications, and can potentially better generalize to novel, unclassified variants. AlphaMissense is one such model, built on the highly successful protein structure prediction model, AlphaFold. AlphaMissense has shown great performance in benchmarks of functional and clinical data, outperforming many supervised models that were trained on similar data. However, like other in silico predictors, AlphaMissense has notable limitations. As a large deep learning model, it lacks interpretability, does not assess the functional impact of variants, and provides pathogenicity scores that are not disease specific. Improving interpretability and precision in computational tools for variant interpretation remains a promising area for advancing clinical genetics.
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