This paper is in the following e-collection/theme issue:

Genome Analysis and Visualization (14) Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Artificial Intelligence (1421) Clinical Informatics (1050)

Published on in Vol 3, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37701, first published .
Diagnosis of a Single-Nucleotide Variant in Whole-Exome Sequencing Data for Patients With Inherited Diseases: Machine Learning Study Using Artificial Intelligence Variant Prioritization

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

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

Authors of this article:

Yu-Shan Huang1 Author Orcid Image ;   Ching Hsu2 Author Orcid Image ;   Yu-Chang Chune1 Author Orcid Image ;   I-Cheng Liao1 Author Orcid Image ;   Hsin Wang2 Author Orcid Image ;   Yi-Lin Lin3 Author Orcid Image ;   Wuh-Liang Hwu4 Author Orcid Image ;   Ni-Chung Lee3 Author Orcid Image ;   Feipei Lai1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Alganmi N, Bashanfar A, Alotaibi R, Banjar H, Karim S, Mirza Z, Abusamra H, Al-Attas M, Turkistany S, Abuzenadah A. Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population. PeerJ Computer Science 2024;10:e1942 View