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» Gene selection for classification of microarray data based o...
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BMCBI
2010
164views more  BMCBI 2010»
13 years 2 months ago
Merged consensus clustering to assess and improve class discovery with microarray data
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
ESANN
2008
13 years 6 months ago
A method for robust variable selection with significance assessment
Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need ...
Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Ales...
BMCBI
2010
92views more  BMCBI 2010»
13 years 5 months ago
Integrating gene expression and GO classification for PCA by preclustering
Background: Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per g...
Jorn R. de Haan, Ester Piek, René C. van Sc...
BMCBI
2007
197views more  BMCBI 2007»
13 years 5 months ago
Boolean networks using the chi-square test for inferring large-scale gene regulatory networks
Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that...
Haseong Kim, Jae K. Lee, Taesung Park
PR
2008
154views more  PR 2008»
13 years 5 months ago
Data-driven decomposition for multi-class classification
This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Cor...
Jie Zhou, Hanchuan Peng, Ching Y. Suen