Machine learning enables new insights into genetic contributions to liver fat accumulation

Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accur...

Full description

Saved in:

Bibliographic Details
Published in:	Cell genomics Vol. 1; no. 3; p. 100066
Main Authors:	Haas, Mary E., Pirruccello, James P., Friedman, Samuel N., Wang, Minxian, Emdin, Connor A., Ajmera, Veeral H., Simon, Tracey G., Homburger, Julian R., Guo, Xiuqing, Budoff, Matthew, Corey, Kathleen E., Zhou, Alicia Y., Philippakis, Anthony, Ellinor, Patrick T., Loomba, Rohit, Batra, Puneet, Khera, Amit V.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 08.12.2021 Elsevier
Subjects:	Genetics GWAS hepatic steatosis liver fat machine learning MRI NAFLD nonalcoholic fatty liver disease polygenic risk score whole-exome sequencing nonalcoholic fatty liver disease NAFLD GWAS MRI liver fat machine learning whole-exome sequencing polygenic risk score hepatic steatosis
ISSN:	2666-979X, 2666-979X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!