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

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.

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Index Terms

• Applied computing

  • Life and medical sciences

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

    • Machine learning approaches

      • Classification and regression trees

• Information systems

  • Information systems applications

• Mathematics of computing

  • Probability and statistics