{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gomez DA"],"funding":["NICHD NIH HHS","NCRR NIH HHS","National Institutes of Health","NIH HHS"],"pagination":["2021-2026"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12096522"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["182(9)"],"pubmed_abstract":["Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal uniparental disomy (UPD), UBE3A pathogenic variants, or imprinting defects. Current methods of differentiating the etiology require molecular testing, which is sometimes difficult to obtain. Recently, computer-based facial analysis systems have been used to assist in identifying genetic conditions based on facial phenotypes. We sought to understand if the facial-recognition system DeepGestalt could find differences in phenotype between molecular subtypes of AS. Images and molecular data on 261 individuals with AS ranging from 10 months through 32 years were analyzed by DeepGestalt in a cross-validation model with receiver operating characterist"],"journal":["American journal of medical genetics. Part A"],"pubmed_title":["Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning."],"pmcid":["PMC12096522"],"funding_grant_id":["U54 RR019478","U54 HD061222"],"pubmed_authors":["Abdul-Rahman OA","Bird LM","Fleischer N","Gomez DA"],"additional_accession":[]},"is_claimable":false,"name":"Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.","description":"Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal uniparental disomy (UPD), UBE3A pathogenic variants, or imprinting defects. Current methods of differentiating the etiology require molecular testing, which is sometimes difficult to obtain. Recently, computer-based facial analysis systems have been used to assist in identifying genetic conditions based on facial phenotypes. We sought to understand if the facial-recognition system DeepGestalt could find differences in phenotype between molecular subtypes of AS. Images and molecular data on 261 individuals with AS ranging from 10 months through 32 years were analyzed by DeepGestalt in a cross-validation model with receiver operating characterist","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Sep","modification":"2026-06-02T20:59:09.485Z","creation":"2026-04-20T03:14:29.555Z"},"accession":"S-EPMC12096522","cross_references":{"pubmed":["32524756"],"doi":["10.1002/ajmg.a.61720"]}}