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

Genetic engineering (2) Machine Learning (1401) Diabetes Education and Elearning for Patients (105)

Published on in Vol 5, No 1 (2020): Jan-Dec

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2020.05.20.20108035v1, first published .
Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development

Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development

Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development

Authors of this article:

Yichuan Liu1 Author Orcid Image ;   Hui-Qi Qu1 Author Orcid Image ;   Adam S Wenocur1 Author Orcid Image ;   Jingchun Qu1, 2 Author Orcid Image ;   Xiao Chang1 Author Orcid Image ;   Joseph Glessner1 Author Orcid Image ;   Patrick Sleiman1, 3, 4 Author Orcid Image ;   Lifeng Tian1 Author Orcid Image ;   Hakon Hakonarson1, 3, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Karalidou V, Kalfakakou D, Papathanasiou A, Fostira F, Matsopoulos G. MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model. Biomolecules 2022;12(11):1552 View
  2. Rusyaeva N, Golodnikov I, Kononenko I, Nikonova T, Shestakova M. Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus. Diabetes mellitus 2023;26(5):473 View