{"title":"Automated Shared Phenotype Discovery in Undiagnosed Cohorts for Rare Disease Research.","authors":"Aaron J Masino, Ranga Baminiwatte","doi":"10.1109/icmla61862.2024.00154","DOIUrl":null,"url":null,"abstract":"<p><p>Rare disease diagnosis is challenging in large part due to incomplete knowledge of gene-to-phenotype associations. One way to address this is to adopt a gene-to-patient paradigm wherein one selects an in-silico predicted pathogenic variant, identifies individuals with the variant, and then determines if the individuals have a shared phenotype. Most studies following this paradigm determine presence of a shared phenotype through manual review of ontology terms in the patient record. We propose a novel automated method to identify the shared phenotype via genetic search using a fitness function that compares the similarity of phenotype term embeddings generated by advanced NLP models applied to the term's text descriptions. Leveraging Human Phenotype Ontology resources, we generated a library of simulated patients across 5,076 Mendelian diseases. Applying our approach to these simulated disease cohorts, we found that the solution phenotypes included a closely matching term for the majority of terms in the disease phenotype under variable conditions of annotation imprecision and noise. We anticipate these methods can aid gene-to-phenotype association discovery for rare diseases by enabling a scalable gene-to-patient research paradigm.</p>","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2024 ","pages":"1025-1030"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967416/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icmla61862.2024.00154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/4 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Rare disease diagnosis is challenging in large part due to incomplete knowledge of gene-to-phenotype associations. One way to address this is to adopt a gene-to-patient paradigm wherein one selects an in-silico predicted pathogenic variant, identifies individuals with the variant, and then determines if the individuals have a shared phenotype. Most studies following this paradigm determine presence of a shared phenotype through manual review of ontology terms in the patient record. We propose a novel automated method to identify the shared phenotype via genetic search using a fitness function that compares the similarity of phenotype term embeddings generated by advanced NLP models applied to the term's text descriptions. Leveraging Human Phenotype Ontology resources, we generated a library of simulated patients across 5,076 Mendelian diseases. Applying our approach to these simulated disease cohorts, we found that the solution phenotypes included a closely matching term for the majority of terms in the disease phenotype under variable conditions of annotation imprecision and noise. We anticipate these methods can aid gene-to-phenotype association discovery for rare diseases by enabling a scalable gene-to-patient research paradigm.