Linked Life Data

16761095

Source:http://linkedlifedata.com/resource/pubmed/id/16761095

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-6-8
pubmed:abstractText
Although many numerical clustering algorithms have been applied to gene expression data analysis, the essential step is still biological interpretation by manual inspection. The correlation between genetic co-regulation and affiliation to a common biological process is what biologists expect. Here, we introduce some clustering algorithms that are based on graph structure constituted by biological knowledge. After applying a widely used dataset, we compared the result clusters of two of these algorithms in terms of the homogeneity of clusters and coherence of annotation and matching ratio. The results show that the clusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Cluster software, which contains the genes that are most expression correlative and most consistent with biological functions. Moreover, knowledge-guided analysis seems much more applicable than GO-Cluster in a larger dataset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1672-9145
pubmed:author
pubmed:issnType
Print
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
379-84
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Comparisons of graph-structure clustering methods for gene expression data.
pubmed:affiliation
Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology,Huazhong University of Science and Technology, Wuhan 430074, China.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't