Literature DB >> 15720123

Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis.

Feng Luan1, Ruisheng Zhang, Chunyan Zhao, Xiaojun Yao, Mancang Liu, Zhide Hu, Botao Fan.   

Abstract

The support vector machine (SVM), as a novel type of learning machine, was used to develop a classification model of carcinogenic properties of 148 N-nitroso compounds. The seven descriptors calculated solely from the molecular structures of compounds selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The obtained results confirmed the discriminative capacity of the calculated descriptors. The result of SVM (total accuracy of 95.2%) is better than that of LDA (total accuracy of 89.8%).

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Year:  2005        PMID: 15720123     DOI: 10.1021/tx049782q

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  7 in total

1.  Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network.

Authors:  Natalja Fjodorova; Marjana Novič
Journal:  J Comput Aided Mol Des       Date:  2011-12-03       Impact factor: 3.686

2.  SVM approach for predicting LogP.

Authors:  Quan Liao; Jianhua Yao; Shengang Yuan
Journal:  Mol Divers       Date:  2006-09-22       Impact factor: 2.943

3.  Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

Authors:  Kazutoshi Tanabe; Bono Lučić; Dragan Amić; Takio Kurita; Mikio Kaihara; Natsuo Onodera; Takahiro Suzuki
Journal:  Mol Divers       Date:  2010-02-26       Impact factor: 2.943

4.  Prediction of skin sensitization potency using machine learning approaches.

Authors:  Qingda Zang; Michael Paris; David M Lehmann; Shannon Bell; Nicole Kleinstreuer; David Allen; Joanna Matheson; Abigail Jacobs; Warren Casey; Judy Strickland
Journal:  J Appl Toxicol       Date:  2017-01-10       Impact factor: 3.446

5.  Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines.

Authors:  Quan Liao; Jianhua Yao; Shengang Yuan
Journal:  Mol Divers       Date:  2007-04-11       Impact factor: 2.943

6.  Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity.

Authors:  Natalja Fjodorova; Marjana Novič
Journal:  Comput Struct Biotechnol J       Date:  2012-07-01       Impact factor: 7.271

7.  QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds.

Authors:  Tengjiao Fan; Guohui Sun; Lijiao Zhao; Xin Cui; Rugang Zhong
Journal:  Int J Mol Sci       Date:  2018-10-03       Impact factor: 5.923

  7 in total

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