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» Unfolding of Microarray Data
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BMCBI
2005
103views more  BMCBI 2005»
13 years 8 months ago
Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
Background: Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage o...
Hua Liu, Sergey Tarima, Aaron S. Borders, Thomas V...
BMCBI
2011
13 years 3 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
2008
98views more  BMCBI 2008»
13 years 8 months ago
Empirical Bayes models for multiple probe type microarrays at the probe level
Background: When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are...
Magnus Åstrand, Petter Mostad, Mats Rudemo
BMCBI
2010
153views more  BMCBI 2010»
13 years 8 months ago
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or su...
Eva Freyhult, Mattias Landfors, Jenny Önskog,...
BMCBI
2008
94views more  BMCBI 2008»
13 years 8 months ago
A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods
Background: The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less...
Richard D. Pearson