Linked Life Data

12112248

Source:http://linkedlifedata.com/resource/pubmed/id/12112248

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2002-7-11
pubmed:abstractText
New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for genetic epidemiologists will be to develop statistical and computational methods that are able to identify subsets of gene expression variables that classify and predict clinical endpoints. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be prespecified. To address this limitation and the limitation of linearity, we have developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. In the present study, we demonstrate that SDA is capable of identifying combinations of gene expression variables that are able to classify and predict autoimmune diseases.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0741-0395
pubmed:author
pubmed:copyrightInfo
Copyright 2002 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
57-69
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Symbolic discriminant analysis of microarray data in autoimmune disease.
pubmed:affiliation
Program in Human Genetics, Vanderbilt University Medical School, Nashville, Tennessee 37232-0700, USA. moore@phg.mc.vanderbilt.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't