ArticleScience translational medicine2020
AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature.
Article in Science translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.medRxiv : the preprint server for health sciences · 2026Article
- Automated reanalysis of genomic data for rare disease diagnostics at scale.Nature medicine · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- Genetically supported drug target prioritization for rare diseases.Genome medicine · 2026Article
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- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
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- Phenotype-first patient matching with SimPheny identifies diagnostic candidates beyond curated gene associations.medRxiv : the preprint server for health sciences · 2026Article
- Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce.Briefings in bioinformatics · 2025Article
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- geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic Disorders.Molecular genetics & genomic medicine · 2025Article
- Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach.Journal of personalized medicine · 2025Review
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- Identifying Phenotypes for Earlier Diagnosis of Rare Diseases.Studies in health technology and informatics · 2025Article
- An integrative scoring approach for prioritization of rare autism spectrum disorder candidate variants from whole exome sequencing data.Scientific reports · 2025Article
- Genome-wide prediction of dominant and recessive neurodevelopmental disorder-associated genes.American journal of human genetics · 2025Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors.
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Abstract
The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts and downloads and further parses hundreds of thousands of full-text articles in search of information supporting the causality and associated phenotypes of most published genetic variants. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patient's given set of phenotypes. Diagnosis of singleton patients (without relatives' exomes) is the most time-consuming scenario, and AMELIE ranked the causative gene at the very top for 66% of 215 diagnosed singleton Mendelian patients from the Deciphering Developmental Disorders project. Evaluating only the top 11 AMELIE-scored genes of 127 (median) candidate genes per patient resulted in a rapid diagnosis in more than 90% of cases. AMELIE-based evaluation of all cases was 3 to 19 times more efficient than hand-curated database-based approaches. We replicated these results on a retrospective cohort of clinical cases from Stanford Children's Health and the Manton Center for Orphan Disease Research. An analysis web portal with our most recent update, programmatic interface, and code is available at AMELIE.stanford.edu.
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