Mass spectrometry based cancer classification using fuzzy fractal dimensions
Pham, Tuan D. (2007) Mass spectrometry based cancer classification using fuzzy fractal dimensions. In: Rueda, Luis, Mery, Domingo, and Kittler, Josef, (eds.) Progress in Pattern Recognition, Image Analysis and Applications. Lecture Notes in Computer Science, 4756 . Springer, Berlin, Germany, pp. 614-623.
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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.
|Item Type:||Book Chapter (Research - B1)|
|Keywords:||pattern recognition; bioinformatics|
|Date Deposited:||03 Feb 2010 05:40|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 50%
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100%|
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