Articles producció científica> Medicina i Cirurgia

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

  • Dades identificatives

    Identificador: imarina:9182611
  • Autors:

    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
  • Altres:

    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
  • Paraules clau:

    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
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