Autor segons l'article: Castane, Helena; Baiges-Gaya, Gerard; Hernandez-Aguilera, Anna; Rodriguez-Tomas, Elisabet; Fernandez-Arroyo, Salvador; Herrero, Pol; Delpino-Rius, Antoni; Canela, Nuria; Menendez, Javier A.; Camps, Jordi; Joven, Jorge;
Departament: Medicina i Cirurgia
Autor/s de la URV: Baiges Gaya, Gerard / Camps Andreu, Jorge / Castañé Vilafranca, Helena / FERNANDEZ ARROYO, SALVADOR / HERRERO GIL, POL / Joven Maried, Jorge / Rodriguez Tomas, Elisabet
Paraules clau: Ultra performance liquid chromatography Support vector machine Sphingomyelin Review Receiver operating characteristic Proteomics Procedures Prevalence Pathogenesis Obesity Nuclear magnetic resonance imaging Nonalcoholic fatty liver Non-alcoholic fatty liver disease Nash Multiomics Metabolomics Metabolism Metabolic disorder Mass spectrometry Machine learning Liver transplantation Liver cell carcinoma Liver cell Liquid-chromatography Lipotoxicity Lipidomics Lipidome Lipid metabolism Lipid composition Learning algorithm Insulin dependent diabetes mellitus Insulin Hydrophilic interaction chromatography Huntington chorea Humans Human High performance liquid chromatography Gray matter Glycerophospholipid Glucose Gas chromatography Fatty liver Electrospray Dyslipidemia Disease exacerbation Diagnostic accuracy Diabetes mellitus Deep learning Data analysis Chloroform Biological marker Bariatric surgery Artificial intelligence Animals Animal Amino acid metabolism Alzheimer disease Adipose tissue Acylcarnitine
Resum: Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.
Àrees temàtiques: Química Molecular biology Materiais General medicine Farmacia Ensino Biochemistry & molecular biology Biochemistry
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: jorge.camps@urv.cat helena.castane@estudiants.urv.cat gerard.baiges@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat jorge.joven@urv.cat
Identificador de l'autor: 0000-0002-3165-3640 0000-0003-2749-4541
Data d'alta del registre: 2024-07-27
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.mdpi.com/2218-273X/11/3/473
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Biomolecules. 11 (3): 1-21
Referència de l'ítem segons les normes APA: Castane, Helena; Baiges-Gaya, Gerard; Hernandez-Aguilera, Anna; Rodriguez-Tomas, Elisabet; Fernandez-Arroyo, Salvador; Herrero, Pol; Delpino-Rius, Ant (2021). Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview. Biomolecules, 11(3), 1-21. DOI: 10.3390/biom11030473
DOI de l'article: 10.3390/biom11030473
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Journal Publications