• ConceptGen: a gene set enrichm...

*Result*: ConceptGen: a gene set enrichment and gene set relation mapping tool.

Title:
ConceptGen: a gene set enrichment and gene set relation mapping tool.
Authors:
Sartor MA; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu, Mahavisno V, Keshamouni VG, Cavalcoli J, Wright Z, Karnovsky A, Kuick R, Jagadish HV, Mirel B, Weymouth T, Athey B, Omenn GS
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2010 Feb 15; Vol. 26 (4), pp. 456-63. Date of Electronic Publication: 2009 Dec 09.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Software*, Gene Expression Profiling/*methods , Pattern Recognition, Automated/*methods, Neoplasm Metastasis/genetics ; Pancreatic Neoplasms/genetics ; Transforming Growth Factor beta/genetics ; Transforming Growth Factor beta/metabolism ; Animals ; Computational Biology ; Databases, Genetic ; Gene Regulatory Networks ; Humans ; Male ; Oligonucleotide Array Sequence Analysis
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Grant Information:
R01 CA132571 United States CA NCI NIH HHS; U54 DA021519 United States DA NIDA NIH HHS; R01 (CA132571) United States CA NCI NIH HHS; U54 DA021519-01A1 United States DA NIDA NIH HHS
Substance Nomenclature:
0 (Transforming Growth Factor beta)
Entry Date(s):
Date Created: 20091217 Date Completed: 20100511 Latest Revision: 20211020
Update Code:
20260130
PubMed Central ID:
PMC2852214
DOI:
10.1093/bioinformatics/btp683
PMID:
20007254
Database:
MEDLINE

*Further Information*

*Motivation: The elucidation of biological concepts enriched with differentially expressed genes has become an integral part of the analysis and interpretation of genomic data. Of additional importance is the ability to explore networks of relationships among previously defined biological concepts from diverse information sources, and to explore results visually from multiple perspectives. Accomplishing these tasks requires a unified framework for agglomeration of data from various genomic resources, novel visualizations, and user functionality.
Results: We have developed ConceptGen, a web-based gene set enrichment and gene set relation mapping tool that is streamlined and simple to use. ConceptGen offers over 20,000 concepts comprising 14 different types of biological knowledge, including data not currently available in any other gene set enrichment or gene set relation mapping tool. We demonstrate the functionalities of ConceptGen using gene expression data modeling TGF-beta-induced epithelial-mesenchymal transition and metabolomics data comparing metastatic versus localized prostate cancers.*