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» Classification with reject option in gene expression data
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APIN
2010
108views more  APIN 2010»
14 years 9 months ago
A low variance error boosting algorithm
Abstract. This paper introduces a robust variant of AdaBoost, cwAdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with da...
Ching-Wei Wang, Andrew Hunter
ICONIP
2010
14 years 6 months ago
Exploring Features and Classifiers to Classify MicroRNA Expression Profiles of Human Cancer
Recently, some non-coding small RNAs, known as microRNAs (miRNA), have drawn a lot of attention to identify their role in gene regulation and various biological processes. The miRN...
Kyung-Joong Kim, Sung-Bae Cho
121
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ISMB
2008
14 years 11 months ago
Classification of arrayCGH data using fused SVM
Motivation: Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are...
Franck Rapaport, Emmanuel Barillot, Jean-Philippe ...
BMCBI
2006
97views more  BMCBI 2006»
14 years 9 months ago
Selecting normalization genes for small diagnostic microarrays
Background: Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. T...
Jochen Jaeger, Rainer Spang
GECCO
2003
Springer
127views Optimization» more  GECCO 2003»
15 years 2 months ago
Complex Function Sets Improve Symbolic Discriminant Analysis of Microarray Data
Abstract. Our ability to simultaneously measure the expression levels of thousands of genes in biological samples is providing important new opportunities for improving the diagnos...
David M. Reif, Bill C. White, Nancy Olsen, Thomas ...