Conrad, Tim
New statistical algorithms for clinical proteomics
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:DSP:2006:542,
author = {Tim Conrad},
title = {New statistical algorithms for clinical proteomics},
booktitle = {Computational Proteomics},
year = {2006},
editor = {Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
number = {05471},
series = {Dagstuhl Seminar Proceedings},
ISSN = {1862-4405},
publisher = {Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2006/542},
annote = {Keywords: MS, Mass Spectrometry, MALDI-TOF, Fingerprinting, Proteomics}
}
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Keywords: |
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MS, Mass Spectrometry, MALDI-TOF, Fingerprinting, Proteomics |
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Seminar: |
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05471 - Computational Proteomics
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Issue date: |
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2006 |
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Date of publication: |
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03.05.2006 |
