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World J Gastroenterol. Nov 28, 2022; 28(44): 6230-6248
Published online Nov 28, 2022. doi: 10.3748/wjg.v28.i44.6230
Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases
J Alfredo Martínez, Marta Alonso-Bernáldez, Diego Martínez-Urbistondo, Juan A Vargas-Nuñez, Ana Ramírez de Molina, Alberto Dávalos, Omar Ramos-Lopez
J Alfredo Martínez, Marta Alonso-Bernáldez, Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
Diego Martínez-Urbistondo, Department of Internal Medicine, Hospital Universitario HM Sanchinarro, Madrid 28050, Spain
Juan A Vargas-Nuñez, Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
Ana Ramírez de Molina, Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
Alberto Dávalos, Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
Omar Ramos-Lopez, Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
Author contributions: Martínez JA and Alonso-Bernáldez M contributed equally to this work as first co-authors. Martínez JA and Ramos-Lopez O conceived and designed the study; Martínez JA, Alonso-Bernáldez M, and Ramos-Lopez O performed the search of articles and wrote the draft of the manuscript; Martínez-Urbistondo D, Vargas-Nuñez JA, Dávalos A, and Ramos-Lopez O contributed to the analysis and critical interpretation of the data; and all authors read and approved the final manuscript.
Supported by the Community of Madrid and the European Union, through the European Regional Development Fund (ERDF)-REACT-EU resources of the Madrid Operational Program 2014–2020, in the action line of R + D + i projects in response to COVID-19, FACINGLCOVID-CM”; Synergic R&D Projects in New and Emerging Scientific Areas on the Frontier of Science and Interdisciplinary Nature of The Community of Madrid, METAINFLAMATION-Y2020/BIO-6600.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Omar Ramos-Lopez, PhD, Professor, Medicine and Psychology School, Autonomous University of Baja California, Universidad 14418, UABC, Parque Internacional Industrial, Tijuana 22390, Baja California, Mexico. oscar.omar.ramos.lopez@uabc.edu.mx
Received: September 11, 2022
Peer-review started: September 11, 2022
First decision: September 29, 2022
Revised: October 7, 2022
Accepted: November 16, 2022
Article in press: November 16, 2022
Published online: November 28, 2022
Core Tip

Core Tip: Chronic liver disease has become a global burden, and new approaches need to be explored to tackle this disease. In this context, machine learning techniques bring a whole new set of opportunities to study novel approaches and biomarkers for prevention, diagnosis, treatment, and prognosis of inflammatory and virus-related liver diseases. The application of machine learning algorithms constitutes a pivotal piece of personalized medicine, allowing the integration of different phenotypical and genotypical data for a precision outcome concerning inflammatory liver comorbidities in non-communicable and viral diseases.