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DOI: 10.4230/DagSemProc.05471.12
URN: urn:nbn:de:0030-drops-5427
URL: https://drops.dagstuhl.de/opus/volltexte/2006/542/
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Conrad, Tim

New statistical algorithms for clinical proteomics

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05471.ConradTim1.ExtAbstract.542.pdf (0.1 MB)


Abstract

Background: Mass spectrometry based screening methods have
been recently introduced into clinical proteomics. This boosts the development
of a new approach for early disease detection: proteomic pattern analysis.

Aim: Find, analyze and compare proteomic patterns in groups
of patients having different properties such as disease status or
epidemio-logical parameters (e.g. sex, age) with a new pipeline to enhance
sensitivity and specificity.

Problems: Mass data acquired from high-throughput platforms
frequently are blurred and noisy. This extremely complicates the reliable
identification of peaks in general and very small peaks below noise-level in
particular.

Approach: Apply sophisticated signal preprocessing steps
followed by statistical analyzes to purge the raw data and enable the detection
of real signals while maintaining information for tracebacks.
Results: A new analysis pipeline has been developed capable
of finding and analyzing peak patterns discriminating different groups of
patients (e.g. male/female, cancer/healthy). First steps towards distributed
computing approaches have been incorporated in the design.

BibTeX - Entry

@InProceedings{conrad:DagSemProc.05471.12,
  author =	{Conrad, Tim},
  title =	{{New statistical algorithms for clinical proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2006/542},
  URN =		{urn:nbn:de:0030-drops-5427},
  doi =		{10.4230/DagSemProc.05471.12},
  annote =	{Keywords: MS, Mass Spectrometry, MALDI-TOF, Fingerprinting, Proteomics}
}

Keywords: MS, Mass Spectrometry, MALDI-TOF, Fingerprinting, Proteomics
Collection: 05471 - Computational Proteomics
Issue Date: 2006
Date of publication: 03.05.2006


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