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Document
New statistical algorithms for clinical proteomics

Authors: Tim Conrad

Published in: Dagstuhl Seminar Proceedings, Volume 5471, Computational Proteomics (2006)


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.

Cite as

Tim Conrad. New statistical algorithms for clinical proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


Copy BibTex To Clipboard

@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-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.12},
  URN =		{urn:nbn:de:0030-drops-5427},
  doi =		{10.4230/DagSemProc.05471.12},
  annote =	{Keywords: MS, Mass Spectrometry, MALDI-TOF, Fingerprinting, Proteomics}
}
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