Abstract
Classification of human complex diseases such as cancers using high-throughput mass spectrometry data generated by modern proteomic technology has quickly become an attractive topic of research in bioinformatics. However, successful applications of such proteomic strategies for early disease detection are greatly dependent on the effectiveness of computational models for data analysis. Ultimately, the extraction of appropriate features that can represent the identities of different classes plays the frontal critical factor for any difficult classification problems. In addition, another major problem associated with pattern recognition is how to effectively handle a large feature space. This paper addresses these two frontal issues for Mass Spectrometry (MS) classification. We apply two computational prediction models to extract features of MS data and then use vector quantisation to reduce the feature storage. We also introduce the technique of information fusion for classification enhancement. The proposed methodology was tested using an MS-based ovarian cancer dataset and the results were found to be superior to a support vector machine approach using a different feature for the same data.