ArticleHuman genetics2025
An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.
Article in Human genetics, 2025. 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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Who cites it
13 citing papers in PubMed.
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.BMC medical informatics and decision making · 2026Article
- Genomic characterization of resistant and relapsed adult B-cell acute lymphoblastic leukemia.Journal of applied genetics · 2026Article
- Exploring the strengths and limitations of AI-driven variant prioritization versus manual curation in inborn errors of immunity.Frontiers in genetics · 2026Article
- Bridging Genotype to Phenotype inGenes · 2025Review
- Yield on Reinterpretation of Genetic Variants in Pediatric Cardiomyopathy.Journal of the American Heart Association · 2025Article
- Article
- Genomics of pediatric cardiomyopathy.Pediatric research · 2025Review
- Evaluating seven bioinformatics platforms for tertiary analysis of genomic data from whole exome sequencing in a pilot group of patients.Advances in laboratory medicine · 2025Article
- Integrating genome and transcriptome analysis to decipher balanced structural variants in unsolved cases of neurodevelopmental disorders.Frontiers in genetics · 2025Article
- Article
- VarChat: the generative AI assistant for the interpretation of human genomic variations.Bioinformatics (Oxford, England) · 2024Article
- The impact of generative artificial intelligence (AI) on the development of personalized pharmaceuticals and the future of precision medicine.EXCLI journal · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.
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