ArticleGenome biology2024
CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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.
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Who cites it
66 citing papers in PubMed, 54 citations in OpenAlex.
- Review
- OpenChallenges: A FAIR-aligned, centralized platform for crowdsourced challenges in biomedical research.Patterns (New York, N.Y.) · 2026Article
- End-to-end deep learning methods for genetic risk prediction of schizophrenia.Nature communications · 2026Article
- Likelihood-based calibration improves the clinical utility of JAG1 functional data for variant classification.American journal of human genetics · 2026Article
- PIEZO1-mediated mechanosensation links aging to bladder dysfunction.bioRxiv : the preprint server for biology · 2026Article
- Gene- and domain-aware calibration increases the clinical utility of variant effect predictors.Research square · 2026Article
- 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
- Calibration of in-frame indel variant effect predictors for clinical variant classification.bioRxiv : the preprint server for biology · 2026Article
- Personalized Medicine, Storied Past, Contentious Present, Promising Future.Journal of personalized medicine · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Ancestry-specific performance of variant effect predictors in clinical variant classification.bioRxiv : the preprint server for biology · 2026Article
- Article
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- EnhancerMatcher: comparing cell-type-specific enhancer activity of DNA sequences using deep convolutional neural networks and explainable AI.NAR genomics and bioinformatics · 2025Article
- Genome region aware CADD thresholds for noncoding variant prioritization.NAR genomics and bioinformatics · 2025Article
- Inferring binding specificities of human transcription factors with the wisdom of crowds.bioRxiv : the preprint server for biology · 2025Article
- Stratifying variant deleteriousness and trait-modulating effect under human recent adaptation using the FIND model.Genome biology · 2025Article
- GHIST 2024: The First Genomic History Inference Strategies Tournament.Molecular biology and evolution · 2025Article
6 more citing papers are in PubMed but not listed here.
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1 author at 1 institution in 1 country.
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Abstract
backgroundThe Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.
resultsPerformance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.
conclusionsResults show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
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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.