License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.07131.3
URN: urn:nbn:de:0030-drops-11329
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2007/1132/
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Schleif, Frank Michael

Advances in pre-processing and model generation for mass spectrometric data analysis

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07131.SchleifFrankMichael.Paper.1132.pdf (0.6 MB)

Abstract

The analysis of complex signals as obtained by mass spectrometric measurements
is complicated and needs an appropriate representation of the data. Thereby
the kind of preprocessing, feature extraction as well as the used similarity measure
are of particular importance. Focusing on biomarker analysis and taking the
functional nature of the data into account this task is even more complicated.
A new mass spectrometry tailored data preprocessing is shown, discussed and analyzed in
a clinical proteom study compared to a standard setting.


BibTeX - Entry

@InProceedings{schleif:DagSemProc.07131.3,
  author =	{Schleif, Frank Michael},
  title =	{{Advances in pre-processing and model generation for mass spectrometric data analysis}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--24},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2007/1132},
  URN =		{urn:nbn:de:0030-drops-11329},
  doi =		{10.4230/DagSemProc.07131.3},
  annote =	{Keywords: Similarity measures, functional data, proteomics, mass spectrometry, pre-processing,wavelet analysis, generalized peak list}
}

Keywords: Similarity measures, functional data, proteomics, mass spectrometry, pre-processing,wavelet analysis, generalized peak list
Collection: 07131 - Similarity-based Clustering and its Application to Medicine and Biology
Issue Date: 2007
Date of publication: 12.09.2007

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