ArticleHuman mutation2019
CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.
Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-evolutionary exome analysis identifies novel type 2 diabetes mellitus genes in the UK Biobank and all of us.PLoS genetics · 2025Pooled it
- Ultra-rare functional variants reveal early-onset breast cancer risk genes and pathways in the UK Biobank and All of Us Research Program.American journal of human genetics · 2026Article
- Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.Human genetics · 2025Article
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects.Nature communications · 2025Article
- Variants in ZFX are associated with an X-linked neurodevelopmental disorder with recurrent facial gestalt.American journal of human genetics · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease.Journal of the American Heart Association · 2023Article
- ShinyBioHEAT: an interactive shiny app to identify phenotype driver genes in E.coli and B.subtilis.Bioinformatics (Oxford, England) · 2023Article
- Functional variants identify sex-specific genes and pathways in Alzheimer's Disease.Nature communications · 2023Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Identification of risk genes for Alzheimer's disease by gene embedding.Cell genomics · 2022Article
- EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.Nucleic acids research · 2022Article
- Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli.Nature communications · 2022Article
- A general calculus of fitness landscapes finds genes under selection in cancers.Genome research · 2022Article
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Identification of Key Regions Mediating Human Melatonin Type 1 Receptor Functional Selectivity Revealed by Natural Variants.ACS pharmacology & translational science · 2021Article
- Uncovering DNA-PKcs ancient phylogeny, unique sequence motifs and insights for human disease.Progress in biophysics and molecular biology · 2021Article
- Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants.International journal of molecular sciences · 2021Article
- A method to delineate de novo missense variants across pathways prioritizes genes linked to autism.Science translational medicine · 2021Article
Corrections and comments
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2 authors.
Funding
Abstract
Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.
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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 Socratic graph.