Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.chemolab.2012.05.012
Title: In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches
Authors: Rao, H.
Wang, Y.
Zeng, X.
Wang, X.
Liu, Y.
Yin, J.
He, H.
Zhu, F. 
Li, Z.
Keywords: Applicability domain
HPXR
Machine learning approaches
Issue Date: 15-Aug-2012
Citation: Rao, H., Wang, Y., Zeng, X., Wang, X., Liu, Y., Yin, J., He, H., Zhu, F., Li, Z. (2012-08-15). In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches. Chemometrics and Intelligent Laboratory Systems 118 : 271-279. ScholarBank@NUS Repository. https://doi.org/10.1016/j.chemolab.2012.05.012
Abstract: In the current study, computational models for hPXR activators and hPXR non-activators were developed using support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural networks (ANN) algorithms. 73 molecular descriptors used for hPXR activator and hPXR non-activator prediction were selected from a pool of 548 descriptors by using a multi-step hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing method. The y-scrambling method was used to test if there is a chance correlation in the developed SVM model. In the meantime, five-fold cross validation of these machine learning methods results in the prediction accuracies of 87.2-92.5% for hPXR activators and 73.8-87.8% for hPXR non-activators, and the prediction accuracies for external test set are 93.8-95.8% for hPXR activators and 86.7-92.8% for hPXR non-activators. Our study suggested that the tested machine learning methods are potentially useful for hPXR activators identification. © 2012 Elsevier B.V.
Source Title: Chemometrics and Intelligent Laboratory Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/106031
ISSN: 01697439
DOI: 10.1016/j.chemolab.2012.05.012
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