Application of support vector machine (SVM) for prediction toxic activity of different data sets.

Zhao, C Y; Zhang, H X; Zhang, X Y; Liu, M C; Hu, Z D; Fan, B T

Zhao, C Y; Zhang, H X; Zhang, X Y; Liu, M C; Hu, Z D; Fan, B T.

Afiliación

Zhao CY; Department of Chemistry, Lanzhou University, Lanzhou 730000, China.

Toxicology ; 217(2-3): 105-19, 2006 Jan 16.

Article en En | MEDLINE | ID: mdl-16213080

RESUMEN

As a new method, support vector machine (SVM) were applied for prediction of toxicity of different data sets compared with other two common methods, multiple linear regression (MLR) and RBFNN. Quantitative structure-activity relationships (QSAR) models based on calculated molecular descriptors have been clearly established. Among them, SVM model gave the highest q(2) and correlation coefficient R. It indicates that the SVM performed better generalization ability than the MLR and RBFNN methods, especially in the test set and the whole data set. This eventually leads to better generalization than neural networks, which implement the empirical risk minimization principle and may not converge to global solutions. We would expect SVM method as a powerful tool for the prediction of molecular properties.

Asunto(s)

Algoritmos; Contaminantes Ambientales/toxicidad; Biología Computacional/métodos; Bases de Datos como Asunto; Dosificación Letal Mediana; Modelos Lineales; Redes Neurales de la Computación; Relación Estructura-Actividad Cuantitativa; Reproducibilidad de los Resultados

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Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Contaminantes Ambientales Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Toxicology Año: 2006 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda

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