Abstract
Cancer classification using high-throughput mass spectrometry data for early disease detection and prevention has recently become an attractive topic of research in bioinformatics. Recently, several studies have shown that the synergy of proteomic technology and pattern classification techniques is promising for the predictive diagnoses of several cancer diseases. However, the extraction of some effective features that can represent the identities of different classes plays a critical factor for any classification problems involving the analysis of complex data. In this paper we present the concept of a fuzzy fractal dimension that can be utilized as a novel feature of mass spectrometry (MS) data. We then apply vector quantization (VQ) to model the class prototyes using the fuzzy fractal dimensions for classification. The proposed methodology was tested with an MS-based ovarian cancer dataset. Using a simple VQ-based classification rule, the overall average classification rates of the proposed approach were found to be superior to some other methods.