Linked Life Data

15587976

Source:http://linkedlifedata.com/resource/pubmed/id/15587976

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2004-12-13
pubmed:abstractText
A common objective in microarray experiments is to select genes that are differentially expressed between two classes (two treatment groups). Selection of differentially expressed genes involves two steps. The first step is to calculate a discriminatory score that will rank the genes in order of evidence of differential expressions. The second step is to determine a cutoff for the ranked scores. Summary indices of the receiver operating characteristic (ROC) curve provide relative measures for a ranking of differential expressions. This article proposes using the hypothesis-testing approach to compute the raw p-values and/or adjusted p-values for three ROC discrimination measures. A cutoff p-value can be determined from the (ranked) p-values or the adjusted p-values to select differentially expressed genes. To quantify the degree of confidence in the selected top-ranked genes, the conditional false discovery rate (FDR) over the selected gene set and the "Type I" (false positive) error probability for each selected gene are estimated. The proposed approach is applied to a public colon tumor data set for illustration. The selected gene sets from three ROC summary indices and the commonly used two-sample t-statistic are applied to the sample classification to evaluate the predictability of the four discrimination measures.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1054-3406
pubmed:author
pubmed:issnType
Print
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
985-1003
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Significance analysis of ROC indices for comparing diagnostic markers: applications to gene microarray data.
pubmed:affiliation
Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
pubmed:publicationType
Journal Article, Comparative Study