{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:45:05Z","timestamp":1762868705224,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.<\/jats:p>","DOI":"10.3390\/diagnostics11122197","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T01:49:58Z","timestamp":1638150598000},"page":"2197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6847-3777","authenticated-orcid":false,"given":"Jose M.","family":"Celaya-Padilla","sequence":"first","affiliation":[{"name":"Unidad Acad\u00e9mica de Ingenier\u00eda El\u00e9ctrica, Universidad Aut\u00f3noma de Zacatecas, Jard\u00edn Ju\u00e1rez 147, Centro, Zacatecas 98000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0684-6483","authenticated-orcid":false,"given":"Karen E.","family":"Villagrana-Ba\u00f1uelos","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica de Ingenier\u00eda El\u00e9ctrica, Universidad Aut\u00f3noma de Zacatecas, Jard\u00edn Ju\u00e1rez 147, Centro, Zacatecas 98000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1093-9970","authenticated-orcid":false,"given":"Juan Jos\u00e9","family":"Oropeza-Valdez","sequence":"additional","affiliation":[{"name":"Unidad de Investigaci\u00f3n Biom\u00e9dica de Zacatecas, Instituto Mexicano del Seguro Social, Centro, Zacatecas 98000, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0695-5356","authenticated-orcid":false,"given":"Joel","family":"Mon\u00e1rrez-Espino","sequence":"additional","affiliation":[{"name":"Department of Health Research, Christus Muguerza del Parque Hospital Chihuahua, University of Monterrey, San Pedro Garza Garc\u00eda 66238, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2124-2036","authenticated-orcid":false,"given":"Julio E.","family":"Casta\u00f1eda-Delgado","sequence":"additional","affiliation":[{"name":"Unidad de Investigaci\u00f3n Biom\u00e9dica de Zacatecas, Instituto Mexicano del Seguro Social, Centro, Zacatecas 98000, Mexico"},{"name":"C\u00e1tedras-CONACyT, Consejo Nacional de Ciencia y Tecnolog\u00eda, Ciudad de M\u00e9xico 03940, 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