<?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>0123-7799</journal-id>
<journal-title><![CDATA[TecnoLógicas]]></journal-title>
<abbrev-journal-title><![CDATA[TecnoL.]]></abbrev-journal-title>
<issn>0123-7799</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico Metropolitano - ITM]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-77992021000300197</article-id>
<article-id pub-id-type="doi">10.22430/22565337.2088</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Support Vector Machines for Biomarkers Detection in in vitro and in vivo Experiments of Organochlorines Exposure]]></article-title>
<article-title xml:lang="es"><![CDATA[Máquinas de vectores de soporte para detección de biomarcadores en experimentos in vitro e in vivo de exposición a organoclorados]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lopera-Rodríguez]]></surname>
<given-names><![CDATA[Jorge Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zuluaga]]></surname>
<given-names><![CDATA[Martha]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jaramillo-Garzón]]></surname>
<given-names><![CDATA[Jorge Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico Metropolitano  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Abierta y a Distancia  ]]></institution>
<addr-line><![CDATA[Dosquebradas ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Caldas  ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>24</volume>
<numero>52</numero>
<fpage>197</fpage>
<lpage>211</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-77992021000300197&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-77992021000300197&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-77992021000300197&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Metabolomic studies generate large amounts of data, whose complexity increases if they are derived from in vivo experiments. As a result, analysis methods highly used in metabolomics, such as Partial Least Squares Discriminant Analysis (PLS-DA), can have particular difficulties with this type of data. However, there is evidence that indicates that Support Vector Machines (SVMs) can better deal with complex data. On the other hand, chronic exposure to organochlorines is a public health problem. It has been associated with diseases such as cancer. Therefore, its identification is relevant to reduce their impact on human health. This study explores the performance of SVMs in classifying metabolic profiles and identifying relevant metabolites in studies of exposure to organochlorines. For this purpose, two experiments were conducted: in the first one, organochlorine exposure was evaluated in HepG2 cells; and, in the second one, it was evaluated in serum samples of agricultural workers exposed to pesticides. The performance of SVMs was compared with that of PLS-DA. Four kernel functions were assessed in SVMs, and the accuracy of both methods was evaluated using a k-fold cross-validation test. In order to identify the most relevant metabolites, Recursive Feature Elimination (RFE) was used in SVMs and Variable Importance in Projection (VIP) in PLS-DA. The results show that SVMs exhibit a higher percentage of accuracy with fewer training samples and better performance in classifying the samples from the exposed agricultural workers. Finally, a workflow based on SVMs for the identification of biomarkers in samples with high biological complexity is proposed.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los estudios en metabolómica generan gran cantidad de datos cuya complejidad aumenta si surgen de experimentos in vivo. A pesar de esto, métodos ampliamente usados en metabolómica como el análisis discriminante por mínimos cuadrados parciales (PLS-DA) tienen dificultades con este tipo de datos, sin embargo, hay evidencia que las máquinas de vectores de soporte (SVM) pueden tener un mejor desempeño. Por otro lado, la exposición crónica a organoclorados es un problema de salud pública. Esta se asocia a enfermedades como el cáncer. Identificarla exposición es relevante para disminuir su impacto. Este estudio tuvo como objetivo explorar el rendimiento de las SVM en la clasificación de perfiles metabolómicos e identificación de metabolitos relevantes en estudios de exposición a organoclorados. Se realizaron dos experimentos: primero se evaluó la exposición a organoclorados en células HepG2. Luego, se evaluó la exposición a pesticidas en muestras de suero de trabajadores agrícolas. El rendimiento de las SVM se comparó con PLS-DA. Se evaluaron cuatro funciones kernel en SVM y la precisión de ambos métodos se evaluó mediante prueba de validación cruzada k-fold. Para identificar los metabolitos relevantes, se utilizó eliminación recursiva de características (RFE) en SVM y la proyección de importancia de variables (VIP) se usó en PLS-DA. Los resultados mostraron que las SVM tuvieron mayor precisión en la clasificación de los trabajadores agrícolas expuestos usando menos muestras de entrenamiento. Se propone un flujo de trabajo basado en SVM que permita la identificación de biomarcadores en muestras con alta complejidad biológica.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Organochlorines]]></kwd>
<kwd lng="en"><![CDATA[Recursive feature elimination]]></kwd>
<kwd lng="en"><![CDATA[Multivariate statistical methods]]></kwd>
<kwd lng="en"><![CDATA[Support vector machines]]></kwd>
<kwd lng="en"><![CDATA[Metabolomics]]></kwd>
<kwd lng="es"><![CDATA[Organoclorados]]></kwd>
<kwd lng="es"><![CDATA[Eliminación Recursiva de Características]]></kwd>
<kwd lng="es"><![CDATA[Estadística Multivariada]]></kwd>
<kwd lng="es"><![CDATA[Máquinas de Vectores de Soporte]]></kwd>
<kwd lng="es"><![CDATA[Metabolómica]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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