Support Vector Machines for Biomarkers Detection in in vitro and in vivo Experiments  of Organochlorines Exposure

Lopera-Rodríguez, Jorge Alejandro; Zuluaga, Martha; Jaramillo-Garzón , Jorge Alberto

Support Vector Machines for Biomarkers Detection in in vitro and in vivo Experiments of Organochlorines Exposure

dc.creator	Lopera-Rodríguez, Jorge Alejandro
dc.creator	Zuluaga, Martha
dc.creator	Jaramillo-Garzón , Jorge Alberto
dc.date	2021-12-16
dc.date.accessioned	2025-10-01T23:52:44Z
dc.description	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.	en-US
dc.description	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.	es-ES
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dc.identifier	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2088
dc.identifier	10.22430/22565337.2088
dc.identifier.uri	https://hdl.handle.net/20.500.12622/7797
dc.language	eng
dc.publisher	Instituto Tecnológico Metropolitano (ITM)	es-ES
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dc.relation	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2088/2227
dc.relation	https://revistas.itm.edu.co/index.php/tecnologicas/article/view/2088/2248
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dc.rights	Derechos de autor 2021 TecnoLógicas	es-ES
dc.source	TecnoLógicas; Vol. 24 No. 52 (2021); e2088	en-US
dc.source	TecnoLógicas; Vol. 24 Núm. 52 (2021); e2088	es-ES
dc.source	2256-5337
dc.source	0123-7799
dc.subject	Organochlorines	en-US
dc.subject	Recursive feature elimination	en-US
dc.subject	Multivariate statistical methods	en-US
dc.subject	Support vector machines	en-US
dc.subject	Metabolomics	en-US
dc.subject	Organoclorados	es-ES
dc.subject	Eliminación Recursiva de Características	es-ES
dc.subject	Estadística Multivariada	es-ES
dc.subject	Máquinas de Vectores de Soporte	es-ES
dc.subject	Metabolómica	es-ES
dc.title	Support Vector Machines for Biomarkers Detection in in vitro and in vivo Experiments of Organochlorines Exposure	en-US
dc.title	Máquinas de vectores de soporte para detección de biomarcadores en experimentos in vitro e in vivo de exposición a organoclorados	es-ES
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Research Papers	en-US
dc.type	Artículos de investigación	es-ES