ArticleHuman mutation2017
Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges.
Article in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04750330 (Mitochondrial DNA and Nuclear SNPs to Predict Severity of COVID-19 Infection), which is not on this map. Cited by 34 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Mitochondrial DNA and Nuclear SNPs to Predict Severity of COVID-19 Infection
Who cites it
34 citing papers in PubMed.
- Comprehensive in Silico Reclassification of MECP2 Variants of Uncertain Significance in Rett Syndrome: Performance Evaluation and Structural Analysis.Journal of molecular neuroscience : MN · 2025Article
- Accurate identification and mechanistic evaluation of pathogenic missense variants withProceedings of the National Academy of Sciences of the United States of America · 2025Article
- A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?Familial cancer · 2025Review
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.Human genetics · 2025Article
- AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.Human genomics · 2024Article
- Rare heterozygous variants in paediatric steroid resistant nephrotic syndrome - a population-based analysis of their significance.Scientific reports · 2024Article
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.bioRxiv : the preprint server for biology · 2024Article
- Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome.Scientific reports · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.Briefings in bioinformatics · 2023Review
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models.Human genetics · 2022Article
- ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP.Scientific reports · 2022Article
- Calculating genetic risk for dysfunction in pleiotropic biological processes using whole exome sequencing data.Journal of neurodevelopmental disorders · 2022Article
- Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity.American journal of human genetics · 2022Article
- Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.Cell reports. Medicine · 2021Article
- Functional prediction and frequency of coding variants in human ACE2 at binding sites with SARS-CoV-2 spike protein on different populations.Journal of medical virology · 2021Article
- Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Two new missense mutations in the protein interaction ASH domain of OCRL1 identified in patients with Lowe syndrome.Intractable & rare diseases research · 2020Article
- Novel compound heterozygous mutations of CLDN16 in a patient with familial hypomagnesemia with hypercalciuria and nephrocalcinosis.Molecular genetics & genomic medicine · 2020Article
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3 authors.
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Abstract
The steady advances in machine learning and accumulation of biomedical data have contributed to the development of numerous computational models that assess the impact of missense variants. Different methods, however, operationalize impact differently. Two common tasks in this context are the prediction of the pathogenicity of variants and the prediction of their effects on a protein's function. These are related but distinct problems, and it is unclear whether methods developed for one are optimized for the other. The Critical Assessment of Genome Interpretation (CAGI) experiment provides a means to address this question empirically. To this end, we participated in various protein-specific challenges in CAGI with two objectives in mind. First, to compare the performance of methods in the MutPred family with the state-of-the-art. Second and more importantly, to investigate the applicability of general-purpose pathogenicity predictors to the classification of specific function-altering variants without additional training or calibration. We find that our pathogenicity predictors performed competitively with other methods, outputting score distributions in agreement with experimental outcomes. Overall, we conclude that binary classifiers learned from disease-causing mutations are capable of modeling important aspects of the underlying biology and the alteration of protein function resulting from mutations.
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