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BMCBI
2010
164views more  BMCBI 2010»
13 years 5 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
2006
154views more  BMCBI 2006»
13 years 7 months ago
An improved procedure for gene selection from microarray experiments using false discovery rate criterion
Background: A large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the p-values of a proper statistical test are o...
James J. Yang, Mark C. K. Yang
ICML
2009
IEEE
14 years 8 months ago
Partially supervised feature selection with regularized linear models
This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be availa...
Thibault Helleputte, Pierre Dupont
BMCBI
2010
115views more  BMCBI 2010»
13 years 7 months ago
Assessment and optimisation of normalisation methods for dual-colour antibody microarrays
Background: Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour a...
Martin Sill, Christoph Schroder, Jörg D. Hohe...
BMCBI
2006
147views more  BMCBI 2006»
13 years 7 months ago
Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data
Background: Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find...
Alex Lewin, Ian C. Grieve