Linked Life Data

12153320

Source:http://linkedlifedata.com/resource/pubmed/id/12153320

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2002-8-2
pubmed:abstractText
The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high-performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least-squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
8756-7938
pubmed:author
pubmed:issnType
Print
pubmed:volume
18
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
847-54
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:articleTitle
Optimal approach for classification of acute leukemia subtypes based on gene expression data.
pubmed:affiliation
Department of Chemical Engineering, Pohang University of Science and Technology, San 31 Hyoja-Dong, Pohang 790-784, Korea, P&I Consulting Company, Ltd., San 31 Hyoja-Dong, Pohang 790-784, Korea.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't