ArticleScientific reports2022
A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Citywide premarital genomic screening in a Middle Eastern population.Nature medicine · 2026Article
- Genomic characterization of resistant and relapsed adult B-cell acute lymphoblastic leukemia.Journal of applied genetics · 2026Article
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- Exploring the strengths and limitations of AI-driven variant prioritization versus manual curation in inborn errors of immunity.Frontiers in genetics · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Neurodevelopmental Phenotype Associated withGenes · 2025Article
- Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce.Briefings in bioinformatics · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.Heart rhythm O2 · 2025Article
- Next-Generation Sequencing: A Review of Its Transformative Impact on Cancer Diagnosis, Treatment, and Resistance Management.Diagnostics (Basel, Switzerland) · 2025Review
- Mutational landscapes of HNF MODY gene products display a wide distribution with functional implications.Endocrine connections · 2025Review
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Pathogenic KCNH2 variant in monozygotic twins with speech delay and lower risk type 2 long QT syndrome.Neurogenetics · 2025Article
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- Digenic variant interpretation with hypothesis-driven explainable AI.NAR genomics and bioinformatics · 2025Article
- DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.BMC bioinformatics · 2025Article
- An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.Human genetics · 2025Article
- Genomics and multiomics in the age of precision medicine.Pediatric research · 2025Review
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
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5 authors.
Funding
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Abstract
Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.
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