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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Veröffentlicht in:	International journal of molecular sciences Jg. 25; H. 11; S. 5965
Hauptverfasser:	Mouskeftara, Thomai, Kalopitas, Georgios, Liapikos, Theodoros, Arvanitakis, Konstantinos, Germanidis, Georgios, Gika, Helen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.06.2024 MDPI
Schlagworte:	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-Zugang:	Volltext
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