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Analysis and understanding of high-dimensionality data by means of multivariate data analysis

Norden, B; Broberg, P; Lindberg, C and Plymoth, Amelie LU (2005) In Chemistry and Biodiversity 2(11). p.1487-1494
Abstract
Multivariate analysis such as principal-components analysis (PCA) and partial-least-squares-discriminant analysis (PLS-DA) have been applied to peptidomics data from clinical urine samples subjected to LC/MS analysis. We show that it is possible to use these methods to get information from a complex set of clinical data. The aim of the work is to use this information as a first step in the further search for clinical biomarker data. It is possible to identify peptide-biomarker fingerprints related to disease diagnosis and progression. Further, we review clinical proteomics and pharmacogenomics data analyzed with the same multivariate approach.
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Chemistry and Biodiversity
volume
2
issue
11
pages
1487 - 1494
publisher
Verlag Helvetica Chimica Acta
external identifiers
  • wos:000233692500007
  • scopus:28844504146
ISSN
1612-1872
DOI
10.1002/cbdv.200590120
language
English
LU publication?
yes
id
d710a056-c12d-4576-aaf3-45c6ec4f16ca (old id 211870)
date added to LUP
2007-08-03 15:42:10
date last changed
2017-09-03 03:56:29
@article{d710a056-c12d-4576-aaf3-45c6ec4f16ca,
  abstract     = {Multivariate analysis such as principal-components analysis (PCA) and partial-least-squares-discriminant analysis (PLS-DA) have been applied to peptidomics data from clinical urine samples subjected to LC/MS analysis. We show that it is possible to use these methods to get information from a complex set of clinical data. The aim of the work is to use this information as a first step in the further search for clinical biomarker data. It is possible to identify peptide-biomarker fingerprints related to disease diagnosis and progression. Further, we review clinical proteomics and pharmacogenomics data analyzed with the same multivariate approach.},
  author       = {Norden, B and Broberg, P and Lindberg, C and Plymoth, Amelie},
  issn         = {1612-1872},
  language     = {eng},
  number       = {11},
  pages        = {1487--1494},
  publisher    = {Verlag Helvetica Chimica Acta},
  series       = {Chemistry and Biodiversity},
  title        = {Analysis and understanding of high-dimensionality data by means of multivariate data analysis},
  url          = {http://dx.doi.org/10.1002/cbdv.200590120},
  volume       = {2},
  year         = {2005},
}