Linked Life Data

17870538

Source:http://linkedlifedata.com/resource/pubmed/id/17870538

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
24
pubmed:dateCreated
2007-10-22
pubmed:abstractText
In this work some chemometrics methods were applied for modeling and prediction of the induction of apoptosis by 4-aryl-4-H-chromenes with descriptors calculated from the molecular structure alone. The genetic algorithm (GA) and stepwise multiple linear regression methods were used to select descriptors which are responsible for the apoptosis-inducing activity of these compounds. Then support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure-activity relationship models. The obtained results using SVM were compared with ANN and MLR; it revealed that the GA-SVM model was much better than other models. The root-mean-square errors of the training set and the test set for GA-SVM model are 0.181, 0.241 and the correlation coefficients were 0.950, 0.924, respectively, and the obtained statistical parameters of cross validation test on GA-SVM model were Q(2)=0.71 and SRESS=0.345 which revealed the reliability of this model. The results were also compared with previous published model and indicate the superiority of the present GA-SVM model.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1464-3391
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
7746-54
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine.
pubmed:affiliation
Department of Chemistry, Mazandaran University, Babolsar, Iran.
pubmed:publicationType
Journal Article