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

Authors: Frank Michael Schleif

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)

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.

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Frank Michael Schleif. Advances in pre-processing and model generation for mass spectrometric data analysis. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@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/entities/document/10.4230/DagSemProc.07131.3},
  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}
}

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Documents authored by Schleif, Frank Michael

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

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