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Diagnosis of a Single-Nucleotide Variant in Whole-Exome Sequencing Data for Patients With Inherited Diseases: Machine Learning Study Using Artificial Intelligence Variant Prioritization.

Yu-Shan HuangChing HsuYu-Chang ChuneI-Cheng LiaoHsin WangYi-Lin LinPaul Wuh-Liang HwuHung-Chang LeeFeipei Lai
Published in: JMIR bioinformatics and biotechnology (2022)
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.
Keyphrases
  • big data
  • artificial intelligence
  • machine learning
  • electronic health record
  • deep learning
  • copy number
  • case report
  • data analysis
  • single cell
  • gene expression
  • genome wide
  • health information