by Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi
Abstract:
Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses.
Reference:
Fast gene ontology based clustering for microarray experiments. (Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi), In BioData mining, volume 1, 2008.
Bibtex Entry:
@article{Ovaska2008,
abstract = {Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses.},
author = {Ovaska, Kristian and Laakso, Marko and Hautaniemi, Sampsa},
doi = {10.1186/1756-0381-1-11},
issn = {1756-0381},
journal = {BioData mining},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jan,
number = {1},
pages = {11},
pmid = {19025591},
title = {{Fast gene ontology based clustering for microarray experiments.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2613876\&tool=pmcentrez\&rendertype=abstract},
volume = {1},
year = {2008}
}