Biomarker Selection for Predicting Alzheimer Disease Using High-Resolution MALDI-TOF Data

High-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in analyzing MALDI-TOF data is the large number of mass/charge peaks (a.k.a. features, data points). With such a huge number of data points for a single sample, efficient feature selection is critical for unequivocal protein pattern discovery. In this paper, we propose a feature selection method and a new biclassification algorithm based on error-correcting output coding (ECOC) in multiclass problems. Our scheme is applied to the analysis of alzheimer´s disease (AD) data. To validate the performance of the proposed algorithm, experiments are performed in comparison with other methods. We show that our proposed framework outperforms not only the standard ECOC framework but also other algorithms.