Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

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;

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Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview - imarina:9182611

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https://hdl.handle.net/20.500.11797/imarina9182611

Autor/es de la URV:	Baiges Gaya, Gerard / Camps Andreu, Jorge / Castañé Vilafranca, Helena / FERNANDEZ ARROYO, SALVADOR / HERRERO GIL, POL / Joven Maried, Jorge / Rodriguez Tomas, Elisabet
Autor según el artículo:	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;
Direcció de correo del 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 del autor:	0000-0002-3165-3640 0000-0003-2749-4541
Año de publicación de la revista:	2021
Tipo de publicación:	Journal Publications
Referencia 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
Referencia al articulo segun fuente origial:	Biomolecules. 11 (3): 1-21
Resumen:	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.
DOI del artículo:	10.3390/biom11030473
Enlace a la fuente original:	https://www.mdpi.com/2218-273X/11/3/473
Versión del articulo depositado:	info:eu-repo/semantics/publishedVersion
Acceso a la licencia de uso:	https://creativecommons.org/licenses/by/3.0/es/
Departamento:	Medicina i Cirurgia
URL Documento de licencia:	https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:	Química Molecular biology Materiais General medicine Farmacia Ensino Biochemistry & molecular biology Biochemistry
Palabras clave:	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
Entidad:	Universitat Rovira i Virgili
Fecha de alta del registro:	2024-07-27

Descripción:	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.
Tipo:	Journal Publications
Coautor:	Universitat Rovira i Virgili
Títol:	Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview
Materia:	Biochemistry,Biochemistry & Molecular Biology,Molecular Biology 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 Química Molecular biology Materiais General medicine Farmacia Ensino Biochemistry & molecular biology Biochemistry
Fecha:	2021
Autor:	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
Derechos:	info:eu-repo/semantics/openAccess

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