Document Detail


Directed indices for exploring gene expression data.
MedLine Citation:
PMID:  12691980     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection.
RESULTS: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).
Authors:
Michael LeBlanc; Charles Kooperberg; Thomas M Grogan; Thomas P Miller
Publication Detail:
Type:  Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.; Validation Studies    
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  19     ISSN:  1367-4803     ISO Abbreviation:  Bioinformatics     Publication Date:  2003 Apr 
Date Detail:
Created Date:  2003-04-14     Completed Date:  2003-12-16     Revised Date:  2013-05-20    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  686-93     Citation Subset:  IM    
Affiliation:
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. mikel@crab.org
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Gene Expression Profiling / methods*,  standards*
Genetic Testing / methods*,  standards
Humans
Lymphoma, Non-Hodgkin / genetics*,  mortality*
Models, Genetic
Models, Statistical
Reproducibility of Results
Risk Assessment / methods*,  standards
Sensitivity and Specificity
Survival Analysis
Grant Support
ID/Acronym/Agency:
CA74841/CA/NCI NIH HHS; CA90998/CA/NCI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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