Machine Learning and COVID-19: Lessons from SARS-CoV-2.

Autor:	Avila-Ponce de León U; Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.; Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico., Vazquez-Jimenez A; Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico., Cervera A; Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico., Resendis-González G; Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico., Neri-Rosario D; Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico., Resendis-Antonio O; Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico. oresendis@inmegen.gob.mx.; Coordinación de la Investigación Científica - Red de Apoyo a la Investigación - Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico. oresendis@inmegen.gob.mx.
Jazyk:	angličtina
Zdroj:	Advances in experimental medicine and biology [Adv Exp Med Biol] 2023; Vol. 1412, pp. 311-335.
DOI:	10.1007/978-3-031-28012-2_17
Abstrakt:	Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution. (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu