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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 174-179
Claudia Plant , University for Health Sciences, Biomedical Informatics and Technology (UMIT), Hall in Tirol, Austria
Melanie Osl , University for Health Sciences, Biomedical Informatics and Technology (UMIT), Hall in Tirol, Austria
Bernhard Tilg , University for Health Sciences, Biomedical Informatics and Technology (UMIT), Hall in Tirol, Austria
Christian Baumgartner , University for Health Sciences, Biomedical Informatics and Technology (UMIT), Hall in Tirol, Austria
ABSTRACT
High-throughput mass-spectrometry screening has the potential of superior results in detecting early stage cancer than traditional biomarkers. Proteomic data poses novel challenges for data mining, especially concerning the curse of dimensionality. In this paper, we present a 3-step feature selection framework combining the advantages of efficient filter and effective wrapper techniques. We demonstrate the performance of our framework on two SELDITOF- MS data sets for identifying biomarker candidates in ovarian and prostate cancer.
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CITATION

C. Plant, B. Tilg, M. Osl and C. Baumgartner, "Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 174-179.
doi:10.1109/ICDMW.2006.80
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