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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
ISBN:
9780791859599
No. of Pages:
686
Publisher:
ASME Press
Publication date:
2010
eBook Chapter
41 Using Noise Perturbation along with Ga-SVM to Overcome over Fitting and Identify Biomarker Sets for Colorectal Cancer
By
Jonathan Hernandez
,
Jonathan Hernandez
H. LEE MOFFIT CANCER CENTER AND RESEARCH INSTITUTE
UNIV. OF SOUTH FLORIDA
TAMPA, FL
; jonathan.hernandez@moffitt.org
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John Heine
,
John Heine
H. LEE MOFFIT CANCER CENTER AND RESEARCH INSTITUTE
UNIV. OF SOUTH FLORIDA
TAMPA, FL
; john.heine@moffit.org
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Timothy Yeatman
Timothy Yeatman
H. LEE MOFFIT CANCER CENTER AND RESEARCH INSTITUTE
UNIV. OF SOUTH FLORIDA
TAMPA, FL
; timothy.yeatman@moffitt.org
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Page Count:
8
-
Published:2010
Citation
Mathur, R, Land, WH, Hernandez, J, Schaffer, D, Heine, J, & Yeatman, T. "Using Noise Perturbation along with Ga-SVM to Overcome over Fitting and Identify Biomarker Sets for Colorectal Cancer." Intelligent Engineering Systems through Artificial Neural Networks, Volume 20. Ed. Dagli, CH. ASME Press, 2010.
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This paper describes an ongoing research effort to identify gene sets that predict the survival of colorectal cancer patients based on gene expression data. Since the dataset includes 395 genes (after initial feature reduction) and 122 patients, the issue of over fitting must be addressed. A genetic algorithm (GA) specifically designed for feature set selection is used in combination with a support vector machine (SVM). By evaluating groups of genes as opposed to individual genes, complementary sets are obtained. To combat over fitting, the original measurements are perturbed by noise using variances appropriate to each measurement and an overall gain...
Abstract
Introduction
Methods
Results
Conclusion
Nomenclature
References
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