Linked Life Data

17099935

Source:http://linkedlifedata.com/resource/pubmed/id/17099935

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2006-11-13
pubmed:abstractText
Microarray gene expression data often contains multiple missing values due to various reasons. However, most of gene expression data analysis algorithms require complete expression data. Therefore, accurate estimation of the missing values is critical to further data analysis. In this paper, an Iterated Local Least Squares Imputation (ILLSimpute) method is proposed for estimating missing values. Two unique features of ILLSimpute method are: ILLSimpute method does not fix a common number of coherent genes for target genes for estimation purpose, but defines coherent genes as those within a distance threshold to the target genes. Secondly, in ILLSimpute method, estimated values in one iteration are used for missing value estimation in the next iteration and the method terminates after certain iterations or the imputed values converge. Experimental results on six real microarray datasets showed that ILLSimpute method performed at least as well as, and most of the time much better than, five most recent imputation methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
4
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
935-57
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Iterated local least squares microarray missing value imputation.
pubmed:affiliation
Bioinformatics Research Group, Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. zhipeng@cs.ualberta.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't