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Nam, Dougu
Bioinformatics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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ADGO: analysis of differentially expressed gene sets using composite GO annotation

Cited 23 times inthomson ciCited 21 times inthomson ci
Title
ADGO: analysis of differentially expressed gene sets using composite GO annotation
Author
Nam, DouguKim, Sang-BaeKim, Seon-KyuYang, SungjinKim, Seon-YoungChu, In-Sun
Issue Date
2006-09
Publisher
OXFORD UNIV PRESS
Citation
BIOINFORMATICS, v.22, no.18, pp.2249 - 2253
Abstract
Motivation: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Results: Here we propose a method for discovering composite biological hemes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached ∼34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data.
URI
https://scholarworks.unist.ac.kr/handle/201301/7186
URL
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=33748690756
DOI
10.1093/bioinformatics/btl378
ISSN
1367-4803
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BIO_Journal Papers
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