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» Combined Gene Selection Methods for Microarray Data Analysis
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BMCBI
2010
164views more  BMCBI 2010»
14 years 12 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...
BMCBI
2011
14 years 9 months ago
Empirical Bayesian models for analysing molecular serotyping microarrays
Background: Microarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assay...
Richard Newton, Jason Hinds, Lorenz Wernisch
BMCBI
2006
106views more  BMCBI 2006»
15 years 2 months ago
Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional nor
Background: Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple ...
Henrik Bengtsson, Ola Hössjer
BMCBI
2006
103views more  BMCBI 2006»
15 years 2 months ago
Improving missing value imputation of microarray data by using spot quality weights
Background: Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools requir...
Peter Johansson, Jari Häkkinen
BMCBI
2006
97views more  BMCBI 2006»
15 years 2 months ago
Goulphar: rapid access and expertise for standard two-color microarray normalization methods
Background: Raw data normalization is a critical step in microarray data analysis because it directly affects data interpretation. Most of the normalization methods currently used...
Sophie Lemoine, Florence Combes, Nicolas Servant, ...