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» Identifying Gene Ontology Areas for Automated Enrichment
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CBMS
2005
IEEE
15 years 3 months ago
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into ...
Haiying Wang, Francisco Azuaje, Olivier Bodenreide...
BMCBI
2006
183views more  BMCBI 2006»
14 years 9 months ago
Mining gene expression data by interpreting principal components
Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many insta...
Joseph C. Roden, Brandon W. King, Diane Trout, Ali...
92
Voted
BMCBI
2006
181views more  BMCBI 2006»
14 years 9 months ago
Array2BIO: from microarray expression data to functional annotation of co-regulated genes
Background: There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of A...
Gabriela G. Loots, Patrick S. G. Chain, Shalini Ma...
BMCBI
2006
127views more  BMCBI 2006»
14 years 9 months ago
Using local gene expression similarities to discover regulatory binding site modules
Background: We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to rela...
Bartek Wilczynski, Torgeir R. Hvidsten, Andriy Kry...
BMCBI
2006
122views more  BMCBI 2006»
14 years 9 months ago
Genome comparison using Gene Ontology (GO) with statistical testing
Background: Automated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gen...
Zhaotao Cai, Xizeng Mao, Songgang Li, Liping Wei