Linked Life Data

20210654

Source:http://linkedlifedata.com/resource/pubmed/id/20210654

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-4-9
pubmed:abstractText
This article presents an approach to microarray data analysis using discretised expression values in combination with a methodology of closed item set mining for class labeled data (RelSets). A statistical 2 x 2 factorial design analysis was run in parallel. The approach was validated on two independent sets of two-color microarray experiments using potato plants. Our results demonstrate that the two different analytical procedures, applied on the same data, are adequate for solving two different biological questions being asked. Statistical analysis is appropriate if an overview of the consequences of treatments and their interaction terms on the studied system is needed. If, on the other hand, a list of genes whose expression (upregulation or downregulation) differentiates between classes of data is required, the use of the RelSets algorithm is preferred. The used algorithms are freely available upon request to the authors.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1557-8100
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
177-86
pubmed:dateRevised
2011-8-1
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Gene expression data analysis using closed item set mining for labeled data.
pubmed:affiliation
National Institute of Biology, Department of Biotechnology and Systems Biology, Ljubljana, Slovenia. ana.rotter@nib.si
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't