外显子组测序
外显子组
工作流程
精密医学
先证者
全基因组测序
计算生物学
医学遗传学
计算机科学
人工智能
医学
机器学习
生物信息学
基因组
遗传学
表型
突变
生物
病理
基因
数据库
作者
Linyan Meng,Ruben Attali,Tomer Talmy,Yakir Regev,Niv Mizrahi,Pola Smirin‐Yosef,Liesbeth Vossaert,Christian Taborda,Michael Santana,Ido Machol,Rui Xiao,Hongzheng Dai,Christine M. Eng,Fan Xia,Shay Tzur
标识
DOI:10.1016/j.gim.2023.100830
摘要
Purpose The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an artificial intelligence model, based on machine learning for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory. Methods The automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants. The model was evaluated on 2 cohorts. The model accuracy was determined using a retrospective cohort comprising 180 randomly selected exome cases (57 singletons, 123 trios); all of which were previously diagnosed and solved through manual interpretation. Diagnostic yield with the modified workflow was estimated using a prospective "production" cohort of 334 consecutive clinical cases. Results The model accurately pinpointed all manually reported variants as candidates. The reported variants were ranked in top 10 candidate variants in 98.4% (121/123) of trio cases, in 93.0% (53/57) of single proband cases, and 96.7% (174/180) of all cases. The accuracy of the model was reduced in some cases because of incomplete variant calling (eg, copy number variants) or incomplete phenotypic description. Conclusion The automated model for case analysis assists clinical genetic laboratories in prioritizing candidate variants effectively. The use of such technology may facilitate the interpretation of genomic data for a large number of patients in the era of precision medicine.
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