<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-1124</journal-id>
<journal-title><![CDATA[Revista mexicana de ciencias pecuarias]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. de cienc. pecuarias]]></abbrev-journal-title>
<issn>2007-1124</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-11242025000100179</article-id>
<article-id pub-id-type="doi">10.22319/rmcp.v16i1.6616</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Comparación de métodos de aprendizaje automático para predicción de valores de cría genómicos en características de crecimiento en bovinos Suizo Europeo]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparison of machine learning methods for predicting genomic breeding values for growth traits in Braunvieh cattle]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velez Labrada]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Rodríguez]]></surname>
<given-names><![CDATA[Paulino]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ali Nilforooshan]]></surname>
<given-names><![CDATA[Mohammad]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruíz Flores]]></surname>
<given-names><![CDATA[Agustín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma Chapingo Posgrado en Producción Animal ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Livestock Improvement Corporation  ]]></institution>
<addr-line><![CDATA[Hamilton ]]></addr-line>
<country>New Zealand</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2025</year>
</pub-date>
<volume>16</volume>
<numero>1</numero>
<fpage>179</fpage>
<lpage>193</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-11242025000100179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-11242025000100179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-11242025000100179&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los algoritmos de Aprendizaje Automático (AA) han demostrado ventaja al abordar desafíos asociados con la cantidad y la complejidad de la información, permiten descubrir patrones, realizar análisis eficientes y servir como herramienta para la toma de decisiones. Este estudio, tuvo como objetivo comparar cuatro métodos de AA: redes neuronales artificiales (RN), árboles de regresión (AR), bosques aleatorios (BA) y máquina de soporte vectorial (SVM) para predecir el valor genómico en bovinos Suizo Europeo utilizando registros fenotípicos de pesos al nacimiento (PN), destete (PD) y al año (PA), así como información genómica. Los resultados indican que la capacidad predictiva de los modelos varía según la característica y la cantidad de información disponible. En general, RN, BA y SVM mostraron un desempeño similar, mientras que AR tuvo un desempeño inferior. La metodología SVM destacó como la herramienta con mayor potencial, obteniendo los valores más altos de correlación Pearson entre fenotipos corregidos y valores genéticos predichos para PD. A pesar de un mayor costo computacional, RN tuvo un desempeño razonable, especialmente para PN y PA. La selección del modelo final depende de las necesidades particulares de la aplicación, así como de factores prácticos como la disponibilidad de datos, recursos computacionales y la interpretabilidad; pero en general, RN y SVM surgieron como opciones sólidas en varias categorías.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Machine Learning (ML) algorithms have proven advantageous in addressing challenges associated with the quantity and complexity of information, discovering patterns, performing efficient analyses, and serving as a decision-making tool. The objective of this study was to compare four ML methods -artificial neural networks (NN), regression trees (RT), random forests (RF), and support vector machines (SVM)- for predicting genomic value in European Swiss cattle using phenotypic records of birth weight (BW), weaning weight (WW) and yearling weight (YW), as well as genomic information. The results indicate that the predictive ability of the models varies according to the features and the amount of information available. NN, RF, and SVM exhibited similar performances, while RT underperformed. The SVM methodology stood out as the tool with the greatest potential, achieving the highest values of Pearson correlation between corrected phenotypes and predicted genetic values for WW. Despite its higher computational cost, the NN performed reasonably well, especially for BW and YW. The selection of the final model depends on the specific requirements of the application, as well as on such practical factors as data availability, computational resources, and interpretability; however, in general, the NN and SVM emerged as solid choices in several categories.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Árboles de regresión]]></kwd>
<kwd lng="es"><![CDATA[Bosques aleatorios]]></kwd>
<kwd lng="es"><![CDATA[Redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[Capacidad predictiva]]></kwd>
<kwd lng="en"><![CDATA[Neural networks]]></kwd>
<kwd lng="en"><![CDATA[Predictive capacity]]></kwd>
<kwd lng="en"><![CDATA[Random forests]]></kwd>
<kwd lng="en"><![CDATA[Regression trees]]></kwd>
</kwd-group>
</article-meta>
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