Linked Life Data

16298163

Source:http://linkedlifedata.com/resource/pubmed/id/16298163

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-1-30
pubmed:abstractText
In this paper, we address the peak detection and alignment problem in the analysis of mass spectrometry data. To deal with the peak redundancy problem existing in the MALDI data acquired in the reflectron mode, we propose to use the amplitude modulation technique in peak detection. The alignment of two peak sets is formulated as a non-rigid registration problem and is solved using a robust point matching (RPM) approach. To align multiple peak sets, we first use a super set method to find a common peak set among all peak sets as a standard and then align all peak sets to the standard using the robust point matching approach in a sequential manner (i.e. We align only one peak set to the standard each time, thus reducing the multiple peak set alignment problem to a simpler two peak set alignment problem). Experimental results from a study of ovarian cancer data set show that the quantitative cross-correlation coefficients among technical replicates are increased after peak alignment. Additional comparisons also demonstrate that our method has a similar performance as the hierarchical clustering method, although the implementations of these methods are different.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1476-9271
pubmed:author
pubmed:issnType
Print
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
27-38
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Detecting and aligning peaks in mass spectrometry data with applications to MALDI.
pubmed:affiliation
Department of Molecular Biophysics and Biochemistry, Yale University, Suite 503, 300 George Street, New Haven, CT 06520, USA. weichuan.yu@yale.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural