Diagnosis of a Single-Nucleotide Variant in Whole-Exome Sequencing Data for Patients With Inherited Diseases: Machine Learning Study Using Artificial Intelligence Variant Prioritization
JMIR Bioinformatics and Biotechnology2022Vol. 3(1), pp. e37701–e37701
Citations Over TimeTop 20% of 2022 papers
Yushan Huang, Ching Hsu, Yu-Chang Chune, I-Cheng Liao, Hsin Wang, Yilin Lin, Wuh‐Liang Hwu, Ni‐Chung Lee, Feipei Lai
Abstract
We successfully applied sequencing data from WES and free-text phenotypic information of patient's disease automatically extracted by the keyword extraction tool for model training and testing. By interpreting our model, we identified which features of variants are important. Besides, we achieved a satisfactory result on finding the target variant in our testing data set. After adopting the HPO terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108). The performance of the model is similar to that of manual analysis, and it has been used to help National Taiwan University Hospital with a genetic diagnosis.
Related Papers
- → The GENCODE exome: sequencing the complete human exome(2011)63 cited
- → Clinical Exome Performance for Reporting Secondary Genetic Findings(2014)35 cited
- → Diagnostic Exome Sequencing — Are We There Yet?(2012)26 cited
- → Exome Sequencing in Mendelian Disorders(2010)1 cited
- → Whole-exome sequencing and its applications in the research of hereditary disease(2012)