Linked Life Data

17484297

Source:http://linkedlifedata.com/resource/pubmed/id/17484297

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2007-5-8
pubmed:abstractText
Gene expression profiles obtained from samples of diabetic and normal rats with and without treatments can be used to identify genes that distinguish normal and diabetic individuals and also to evaluate the effectiveness of drug treatments. This study examines changes in global gene expression in rat muscle caused by streptozotocin-induced diabetes and vanadyl sulfate treatment. We explored model-based and algorithm-based methods with gene screening measures for microarray gene expression data to classify and predict individuals with high risk of diabetes. Results show that the mixed ANOVA model-based approach provides an efficient way to conduct an investigation of the inherent variability in gene expression data and to estimate the effects of experimental factors such as treatments and diseases and their interactions. The algorithm-based weighted voting and neural network classifiers show good classification performance for the diabetes and treatment groups. Although neural network performs better than weighted voting with higher classification rate, the interpretation of weighted voting is more straightforward. The study indicates that the choice of the gene selection procedure is at least as important as the choice of the classification procedure. We conclude that both mixed model-based and algorithm-based approaches provide the statistical evidence of the biological hypotheses that vanadyl sulfate treatment of diabetic animals restores gene expression patterns to normal. Although model-based and algorithm-based methods provide different strengths and perspective for the analysis of the same set of data, in general both can be considered and developed for analyzing factorial design experiments with multiple groups and factors. This study represents a major step towards the discovery of responsible genes related to diabetes and its treatment.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0962-2802
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
139-53
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Model-based or algorithm-based? Statistical evidence for diabetes and treatments using gene expression.
pubmed:affiliation
Department of Biostatistics, The State University of New York, Buffalo 14214, USA. yliang@buffalo.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural