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Int. J. Environ. Res. Public Health 2016, 13(11), 1141; doi:10.3390/ijerph13111141

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

1
Department of Public Health, Qingdao University Medical College, Qingdao 266071, China
2
Modern Educational Technology Center, Qingdao University, Qingdao 266071, China
3
Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China
4
Department of Chemistry, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editor: William Chi-shing Cho
Received: 7 September 2016 / Revised: 21 October 2016 / Accepted: 24 October 2016 / Published: 15 November 2016
View Full-Text   |   Download PDF [1568 KB, uploaded 15 November 2016]   |  

Abstract

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. View Full-Text
Keywords: QSAR; aromatic amines; gene expression programming; multilayer perceptrons QSAR; aromatic amines; gene expression programming; multilayer perceptrons
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MDPI and ACS Style

Song, F.; Zhang, A.; Liang, H.; Cui, L.; Li, W.; Si, H.; Duan, Y.; Zhai, H. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs. Int. J. Environ. Res. Public Health 2016, 13, 1141.

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