Linked Life Data

18284666

Source:http://linkedlifedata.com/resource/pubmed/id/18284666

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-4-15
pubmed:abstractText
Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1471-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
106
pubmed:dateRevised
2010-9-22
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
K-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space.
pubmed:affiliation
Research Group for Chemometrics, Department of Chemistry, Umeå University, Umeå, SE-901 87, Sweden. max.bylesjo@chem.umu.se
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't