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In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner
Jing Chen1; Yuan Yan Tang1,2; Bin Fang1; Chang Guo1
2012-05-01
Source PublicationJournal of Molecular Graphics and Modelling
ISSN1093-3263
Volume35Pages:21-27
Abstract

With an increasing need for the rapid and effective safety assessment of compounds in industrial and civil-use products, in silico toxicity exploration techniques provide an economic way for environmental hazard assessment. The previous in silico researches have developed many quantitative structure-activity relationships models to predict toxicity mechanisms for last decade. Most of these methods benefit from data analysis and machine learning techniques, which rely heavily on the characteristics of data sets. For Tetrahymena pyriformis toxicity data sets, there is a great technical challenge - data imbalance. The skewness of data class distribution would greatly deteriorate the prediction performance on rare classes. Most of the previous researches for phenol mechanisms of toxic action prediction did not consider this practical problem. In this work, we dealt with the problem by considering the difference between the two types of misclassifications. Random Forest learner was employed in cost-sensitive learning framework to construct prediction models based on selected molecular descriptors. In computational experiments, both the global and local models obtained appreciable overall prediction accuracies. Particularly, the performance on rare classes was indeed promoted. Moreover, for practical usage of these models, the balance of the two misclassifications can be adjusted by using different cost matrices according to the application goals. 

KeywordCost-sensitive Phenols Qsar Random Forest Toxic Action Mechanisms
DOIhttps://doi.org/10.1016/j.jmgm.2012.01.002
URLView the original
Indexed BySCI
Language英语
WOS Research AreaBiochemistry & Molecular Biology ; Computer Science ; Crystallography ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Biochemistry & Molecular Biology ; Computer Science, Interdisciplinary Applications ; Crystallography ; Mathematical & Computational Biology
WOS IDWOS:000304513400003
PublisherELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
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被引频次[WOS]:9   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.College of Computer Science, Chongqing University, Chongqing 400030, China
2.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
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GB/T 7714
Jing Chen,Yuan Yan Tang,Bin Fang,et al. In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner[J]. Journal of Molecular Graphics and Modelling,2012,35:21-27.
APA Jing Chen,Yuan Yan Tang,Bin Fang,&Chang Guo.(2012).In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner.Journal of Molecular Graphics and Modelling,35,21-27.
MLA Jing Chen,et al."In silico prediction of toxic action mechanisms of phenols for imbalanced data with Random Forest learner".Journal of Molecular Graphics and Modelling 35(2012):21-27.
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