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Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lym...

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Detalles Bibliográficos
Autores principales: Yuan, Hua, Huang, Jianping, Cao, Chenzhong
Formato: Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2738923/
https://www.ncbi.nlm.nih.gov/pubmed/19742136
http://dx.doi.org/10.3390/ijms10073237
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author Yuan, Hua
Huang, Jianping
Cao, Chenzhong
author_facet Yuan, Hua
Huang, Jianping
Cao, Chenzhong
author_sort Yuan, Hua
collection PubMed
description Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.
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spelling pubmed-27389232009-09-08 Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine Yuan, Hua Huang, Jianping Cao, Chenzhong Int J Mol Sci Article Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. Molecular Diversity Preservation International (MDPI) 2009-07-17 /pmc/articles/PMC2738923/ /pubmed/19742136 http://dx.doi.org/10.3390/ijms10073237 Text en © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Yuan, Hua
Huang, Jianping
Cao, Chenzhong
Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
title Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
title_full Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
title_fullStr Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
title_full_unstemmed Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
title_short Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
title_sort prediction of skin sensitization with a particle swarm optimized support vector machine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2738923/
https://www.ncbi.nlm.nih.gov/pubmed/19742136
http://dx.doi.org/10.3390/ijms10073237
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