PMID- 19742136
OWN - NLM
STAT- MEDLINE
DCOM- 20140318
LR  - 20230408
IS  - 1422-0067 (Electronic)
IS  - 1422-0067 (Linking)
VI  - 10
IP  - 7
DP  - 2009 Jul 17
TI  - Prediction of skin sensitization with a particle swarm optimized support vector 
      machine.
PG  - 3237-3254
LID - 10.3390/ijms10073237 [doi]
AB  - 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.
FAU - Yuan, Hua
AU  - Yuan H
AD  - Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of 
      Education, Hunan University of Science and Technology, Xiangtan 411201, China.
AD  - Hunan Provincial University Key Laboratory of QSAR/QSPR, Xiangtan 411201, China.
AD  - School of Chemistry and Chemical Engineering, Hunan University of Science and 
      Technology, Xiangtan 411201, China.
FAU - Huang, Jianping
AU  - Huang J
AD  - Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, 
      Zhejiang University, Hangzhou 310027, China.
FAU - Cao, Chenzhong
AU  - Cao C
AD  - Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of 
      Education, Hunan University of Science and Technology, Xiangtan 411201, China.
AD  - Hunan Provincial University Key Laboratory of QSAR/QSPR, Xiangtan 411201, China.
AD  - School of Chemistry and Chemical Engineering, Hunan University of Science and 
      Technology, Xiangtan 411201, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20090717
PL  - Switzerland
TA  - Int J Mol Sci
JT  - International journal of molecular sciences
JID - 101092791
RN  - 0 (Allergens)
RN  - 0 (Small Molecule Libraries)
SB  - IM
MH  - Allergens/*chemistry/toxicity
MH  - Dermatitis, Occupational/etiology
MH  - Humans
MH  - *Quantitative Structure-Activity Relationship
MH  - Small Molecule Libraries/*chemistry/toxicity
MH  - *Support Vector Machine
PMC - PMC2738923
OTO - NOTNLM
OT  - guinea pig maximization test
OT  - murine local lymph node assay
OT  - particle swarm optimization
OT  - skin sensitization
OT  - support vector machine
EDAT- 2009/09/11 06:00
MHDA- 2009/09/11 06:01
PMCR- 2009/07/17
CRDT- 2009/09/11 06:00
PHST- 2009/05/25 00:00 [received]
PHST- 2009/06/24 00:00 [accepted]
PHST- 2009/09/11 06:00 [entrez]
PHST- 2009/09/11 06:00 [pubmed]
PHST- 2009/09/11 06:01 [medline]
PHST- 2009/07/17 00:00 [pmc-release]
AID - i10073237 [pii]
AID - ijms-10-03237 [pii]
AID - 10.3390/ijms10073237 [doi]
PST - epublish
SO  - Int J Mol Sci. 2009 Jul 17;10(7):3237-3254. doi: 10.3390/ijms10073237.