Classification of Mass Spectrometry Based Protein Markers by Kriging Error Matching

  • Authors:

  • Tuan D. Pham;Honghui Wang;Xiaobo Zhou;Dominik Beck;Miriam Brandl;Gerard Hoehn;Joseph Azok;Marie-Luise Brennan;Stanley L. Hazen;Stephen T. Wong

  • Affiliations:

  • Bioinformatics Applications Research Center, James Cook University, Townsville, Australia QLD 4811;Clinical Center, National Institutes of Health, Bethesda, USA MD 20892;Department of Radiology, Weill Cornell Medical College and Methodist Hospital Research, Houston, USA;Bioinformatics Applications Research Center, James Cook University, Townsville, Australia QLD 4811;Bioinformatics Applications Research Center, James Cook University, Townsville, Australia QLD 4811;Clinical Center, National Institutes of Health, Bethesda, USA MD 20892;Clinical Center, National Institutes of Health, Bethesda, USA MD 20892;Center for Cardiovascular Diagnostics and Prevention Cleveland Clinic Foundation, Cleveland, USA OH 44195;Center for Cardiovascular Diagnostics and Prevention Cleveland Clinic Foundation, Cleveland, USA OH 44195;Department of Radiology, Weill Cornell Medical College and Methodist Hospital Research, Houston, USA

  • Venue:
  • MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
  • Year:
  • 2008

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Abstract

Discovery of biomarkers using serum proteomic patterns is currently one of the most attractive interdisciplinary research areas in computational life science. This new proteomic approach has the clinical significance in being able to detect disease in its early stages and to develop new drugs for disease treatment and prevention. This paper introduces a novel pattern classification strategy for identifying protein biomarkers using mass spectrometry data of blood samples collected from patients in emergency department monitored for major adverse cardiac events within six months. We applied the theory of geostatistics and a kriging error matching scheme for identifying protein biomarkers that are able to provide an average classification rate superior to other current methods. The proposed strategy is very promising as a general computational bioinformatic model for proteomic-pattern based biomarker discovery.