Linked Life Data

16527833

Source:http://linkedlifedata.com/resource/pubmed/id/16527833

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2006-5-24
pubmed:abstractText
MOTIVATION: A common problem in the emerging field of metabolomics is the consolidation of signal lists derived from metabolic profiling of different cell/tissue/fluid states where a number of replicate experiments was collected on each state. RESULTS: We describe an approach for the consolidation of peak lists based on hierarchical clustering, first within each set of replicate experiments and then between the sets of replicate experiments. The problems of finding the dendrogram tree cutoff which gives the optimal number of peak clusters and the effect of different clustering methods were addressed. When applied to gas chromatography-mass spectrometry metabolic profiling data acquired on Leishmania mexicana, this approach resulted in robust data matrices which completely separated the wild-type and two mutant parasite lines based on their metabolic profile.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1391-6
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Progressive peak clustering in GC-MS Metabolomic experiments applied to Leishmania parasites.
pubmed:affiliation
Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't