Linear predictive coding and its decision logic for early prediction of major adverse cardiac events using mass spectrometry data

Authors:
Tuan D. Pham;Honghui Wang;Xiaobo Zhou;Dominik Beck;Miriam Brandl;Gerard Hoehn;Joseph Azok;Marie-Luise Brennan;Stanley L. Hazen;King Li;Stephen T. C. Wong
Affiliations:
Bioinformatics Applications Research Center and James Cook University, Townsville, QLD, Australia;National Institutes of Health, Bethesda, MD;Harvard Medical School, Boston, MA;Bioinformatics Applications Research Center;Bioinformatics Applications Research Center;National Institutes of Health, Bethesda, MD;National Institutes of Health, Bethesda, MD;Cleveland Clinic Foundation, Cleveland, OH;Cleveland Clinic Foundation, Cleveland, OH;Cleveland Clinic Foundation, Cleveland, OH;Harvard Medical School, Boston, MA
Venue:
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Year:
2006

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Abstract

Proteomics is an emerging field of modern biotechnology and an attractive research area in bioinformatics. Protein annotation by mass spectrometry has recently been utilized for the classification and prediction of diseases. In this paper we apply the theory of linear predictive coding and its decision logic for the prediction of major adverse cardiac risk using mass spectra. The new method was tested with a small set of mass spectrometry data. The initial experimental results are found promising for the prediction and show the implication of the potential use of the data for biomarker discovery.