Linked Life Data

11807944

Source:http://linkedlifedata.com/resource/pubmed/id/11807944

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2002-1-24
pubmed:abstractText
One of the standard tools for the analysis of data arranged in matrix form is singular value decomposition (SVD). Few applications to genomic data have been reported to date mainly for the analysis of gene expression microarray data. We review SVD properties, examine mathematical terms and assumptions implicit in the SVD formalism, and show that SVD can be applied to the analysis of matrices representing pairwise alignment scores between large sets of protein sequences. In particular, we illustrate SVD capabilities for data dimension reduction and for clustering protein sequences. A comparison is performed between SVD-generated clusters of proteins and annotation reported in the SWISS-PROT Database for a set of protein sequences forming the calycin superfamily, entailing all entries corresponding to the lipocalin, cytosolic fatty acid-binding protein, and avidin-streptavidin Prosite patterns.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0887-3585
pubmed:author
pubmed:copyrightInfo
Copyright 2001 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
46
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
161-70
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Singular value decomposition analysis of protein sequence alignment score data.
pubmed:affiliation
Dipartimento Scientifico Tecnologico, Facoltà di Scienze, Università di Verona, Verona, Italy. fogolari@sci.univr.it
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't