Linked Life Data

21133039

Source:http://linkedlifedata.com/resource/pubmed/id/21133039

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-12-7
pubmed:abstractText
Reducing redundancy is an important goal for most feature selection methods. Almost all methods for redundancy reduction are based on the correlation between gene expression levels. In this paper, we utilise the knowledge in Gene Ontology to provide a new model for measuring redundancy among genes. We propose a novel similarity measure, which incorporates semantic and expression level similarities. We compare our method with traditional expression value-only similarity model on several public microarray datasets. The experimental results show that our approach is capable of offering higher or the same classification accuracy while providing a smaller gene feature.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1748-5673
pubmed:author
pubmed:issnType
Print
pubmed:volume
4
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
520-34
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Using gene ontology to enhance effectiveness of similarity measures for microarray data.
pubmed:affiliation
Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, A1B 3X5, Canada. zchen@mun.ca
pubmed:publicationType
Journal Article