The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Autor:	Qadri, Sami, Yki-Järvinen, Hannele
Přispěvatelé:	Department of Medicine, Clinicum, HUS Internal Medicine and Rehabilitation, Hannele Yki-Järvinen Research Group
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	eksominlaajuinen assosiaatiotutkimus genetiikka ei-alkoholiperäinen rasvamaksatauti syväoppiminen steatoosi genetic polymorphisms NAFLD biopankki geneettinen polymorfismi steatosis genetics tietokonetomografia kirroosi fibroosi fatty liver liver fibrosis variantti genome-wide association study cirrhosis fibrosis NASH exome-wide association study rasvamaksa deep learning non-alcoholic fatty liver disease computed tomography biobank machine learning koneoppiminen variant maksafibroosi 3121 General medicine internal medicine and other clinical medicine mutaatio ei-alkoholiperäinen steatohepatiitti disease-causing variant non-alcoholic steatohepatitis mutation genominlaajuinen assosiaatiotutkimus
Popis:	Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank. By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fat-associated genetic variants. Non
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=od______1593::6bb1e2a9d2ca61a9a512991a31181b9f http://hdl.handle.net/10138/353505 Zobrazit plný text záznamu