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

Biomolecules

Nuria Canela¹  Salvador Fernández-Arroyo¹  Pol Herrero¹  Antoni Delpino-Rius¹  JavierA. Menendez²  Helena Castañé³  Elisabet Rodríguez-Tomàs³  Anna Hernández-Aguilera³  Jorge Joven³  Jordi Camps³  Gerard Baiges-Gaya³ 

[1] Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (Joint Unit Eurecat-Universitat Rovira i Virgili), Unique Scientific and Technical Infrastructure (ICTS), Av. Universitat 1, 43204 Reus, Spain;Girona Biomedical Research Institute (IDIBGI), C. Dr. Castany s/n, 17190 Salt, Spain;Unitat de Recerca Biomèdica (URB-CRB), Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Spain;

关键词: adipose tissue;    artificial intelligence;    bariatric surgery;    deep learning;    metabolism;    NASH;   

DOI  :  10.3390/biom11030473

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown