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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)
Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer
Hong Kong, China
December 18-December 22
ISBN: 0-7695-2702-7
ASCII Text
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Claudia Plant, Melanie Osl, Bernhard Tilg, Christian Baumgartner, "Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer," Data Mining Workshops, International Conference on, pp. 174-179, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICDMW.2006.80,
author = {Claudia Plant and Melanie Osl and Bernhard Tilg and Christian Baumgartner},
title = {Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer},
journal ={Data Mining Workshops, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2702-7},
pages = {174-179},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.80},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining Workshops, International Conference on
TI - Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer
SN - 0-7695-2702-7
SP174
EP179
A1 - Claudia Plant,
A1 - Melanie Osl,
A1 - Bernhard Tilg,
A1 - Christian Baumgartner,
PY - 2006
KW - null
VL - 0
JA - Data Mining Workshops, International Conference on
ER -
 
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
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.
Citation:
Claudia Plant, Melanie Osl, Bernhard Tilg, Christian Baumgartner, "Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer," icdmw, pp.174-179, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
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