ArticleAmerican journal of human genetics2020
Interpretable Clinical Genomics with a Likelihood Ratio Paradigm.
Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- aiDIVA - hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models.NPJ genomic medicine · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- Rare-Disease Diagnosis on the ZebraMap Multimodal Case Report Dataset: A Hybrid Pipeline with Grounded Explainability.Sensors (Basel, Switzerland) · 2026Article
- Diagnostic Yield After Postnatal Reanalysis of Prenatal Exome Sequencing Results.Prenatal diagnosis · 2026Article
- Reframing AI for Rare Disease Recognition.Research square · 2026Article
- Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis.medRxiv : the preprint server for health sciences · 2026Article
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- GEN-KnowRD: Reframing AI for Rare Disease Recognition.medRxiv : the preprint server for health sciences · 2026Article
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- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- Phenotype-first patient matching with SimPheny identifies diagnostic candidates beyond curated gene associations.medRxiv : the preprint server for health sciences · 2026Article
- GA4GH phenopacket-driven characterization of genotype-phenotype correlations in Mendelian disorders.American journal of human genetics · 2026Article
- Exploring the strengths and limitations of AI-driven variant prioritization versus manual curation in inborn errors of immunity.Frontiers in genetics · 2026Article
- Information content as a health system screening tool for rare diseases.NPJ digital medicine · 2025Article
- Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce.Briefings in bioinformatics · 2025Article
- geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic Disorders.Molecular genetics & genomic medicine · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- PhenoDP: leveraging deep learning for phenotype-based case reporting, disease ranking, and symptom recommendation.Genome medicine · 2025Article
- Enhancing the Accuracy of Human Phenotype Ontology Identification: Comparative Evaluation of Multimodal Large Language Models.Journal of medical Internet research · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25%-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Here, we present an approach to genomic diagnostics that exploits the likelihood ratio (LR) framework to provide an estimate of (1) the posttest probability of candidate diagnoses, (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 case reports comprising 262 Mendelian diseases, and the correct diagnosis had a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.
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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.