Linked Life Data

11473005

Source:http://linkedlifedata.com/resource/pubmed/id/11473005

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2001-7-26
pubmed:abstractText
Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:volume
17 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S157-64
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Feature selection for DNA methylation based cancer classification.
pubmed:affiliation
Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany. Fabian.Model@epigenomics.com
pubmed:publicationType
Journal Article, Comparative Study