Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A "non-invasive alternative" to liver biopsy?

Academic Article

Publication Date:

2019

Iris type:

01.07 Editoriale in rivista

Keywords:

Free fatty acids; Glycomics; Lipidomics; Non-alcoholic fatty liver disease; Non-alcoholic steatohepatitis; Non-invasive diagnostic methods; Predictive models

List of contributors:

Gastaldelli, Amalia

Authors of the University:

GASTALDELLI AMALIA

Handle:

https://iris.cnr.it/handle/20.500.14243/369247

Published in:

METABOLISM, CLINICAL AND EXPERIMENTAL (PRINT)

Journal

Overview

URL

http://www.scopus.com/record/display.url?eid=2-s2.0-85075345070&origin=inward

Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A "non-invasive alternative" to liver biopsy?

Gastaldelli, Amalia

METABOLISM, CLINICAL AND EXPERIMENTAL (PRINT)

Overview

URL