Referenced Items are not available for this document.
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 SELDI-TOF-MS data sets for identifying biomarker candidates in ovarian and prostate cancer
Published in:
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Date of Conference: Dec. 2006
- Page(s):
- 174 - 179
- Print ISBN:
- 0-7695-2702-7
- INSPEC Accession Number:
- 9352899
- Conference Location :
- Hong Kong
- Digital Object Identifier :
- 10.1109/ICDMW.2006.80
- Product Type:
- Conference Publications
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