Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associ...

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Bibliographic Details
Published in:	International journal of molecular sciences Vol. 25; no. 11; p. 5965
Main Authors:	Mouskeftara, Thomai, Kalopitas, Georgios, Liapikos, Theodoros, Arvanitakis, Konstantinos, Germanidis, Georgios, Gika, Helen
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.06.2024 MDPI
Subjects:	Accuracy Adult Algorithms Biomarkers Biomarkers - blood Biopsy Case-Control Studies Classification Enzymes Fatty acids Fatty liver Female Ferritin Health care Homeostasis Humans Inflammation Insulin Investigations Lipid Metabolism Lipidomics - methods Lipids Liver diseases Low density lipoproteins Machine Learning Male Metabolism Middle Aged Non-alcoholic Fatty Liver Disease - blood Non-alcoholic Fatty Liver Disease - metabolism Physiological aspects Plasma Triglycerides Type 2 diabetes Greece Massachusetts non-alcoholic fatty liver disease machine learning plasma untargeted lipidomics
ISSN:	1422-0067, 1661-6596, 1422-0067
Online Access:	Get full text
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