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BMCBI
2006
122views more  BMCBI 2006»
13 years 4 months ago
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Background: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifi...
Carmen Lai, Marcel J. T. Reinders, Laura J. van't ...
CSB
2005
IEEE
143views Bioinformatics» more  CSB 2005»
13 years 10 months ago
Multivariate gene selection: Does it help
When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of ge...
Carmen Lai, Marcel J. T. Reinders
CIKM
2009
Springer
13 years 11 months ago
Multivariate classification of urine metabolome profiles for breast cancer diagnosis
Background: Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular pro...
Younghoon Kim, Imhoi Koo, Byung Hwa Jung, Bong Chu...
BMCBI
2008
169views more  BMCBI 2008»
13 years 4 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...
CSB
2005
IEEE
156views Bioinformatics» more  CSB 2005»
13 years 10 months ago
A Robust Meta-classification Strategy for Cancer Diagnosis from Gene Expression Data
One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combi...
Gabriela Alexe, Gyan Bhanot, Babu Venkataraghavan,...