Linked Life Data

11035779

Source:http://linkedlifedata.com/resource/pubmed/id/11035779

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
22
pubmed:dateCreated
2000-11-28
pubmed:abstractText
We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0027-8424
pubmed:author
pubmed:issnType
Print
pubmed:day
24
pubmed:volume
97
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
12079-84
pubmed:dateRevised
2010-9-14
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Coupled two-way clustering analysis of gene microarray data.
pubmed:affiliation
Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't