Linked Life Data

15807514

Source:http://linkedlifedata.com/resource/pubmed/id/15807514

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2005-4-5
pubmed:abstractText
The use of numerous descriptors that are indicative of molecular structure is becoming common in quantitative structure-activity relationship (QSAR) studies. As all of the descriptors might carry more or less molecular information, it seems more advisable to investigate the possible variable combination rather than variable selection. In this paper, an optimized block-wise variable combination (OBVC) by particle swarm optimization based on partial least squares modeling has been proposed for variable combination. An F statistic is also introduced to determine the dimensionality of the PLS model. The performance is assessed using two QSAR data sets. Experimental results have shown the good performance of this technique compared to those obtained by stepwise regression.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1549-9596
pubmed:author
pubmed:issnType
Print
pubmed:volume
45
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
486-93
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:articleTitle
Optimized block-wise variable combination by particle swarm optimization for partial least squares modeling in quantitative structure-activity relationship studies.
pubmed:affiliation
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't