New statistical algorithms for clinical proteomics

Author Tim Conrad

Thumbnail PDF


  • Filesize: 127 kB
  • 2 pages

Document Identifiers

Author Details

Tim Conrad

Cite AsGet BibTex

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)


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.
  • MS
  • Mass Spectrometry
  • Fingerprinting
  • Proteomics


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads