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» Analysis of Variance for Gene Expression Microarray Data
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BMCBI
2006
103views more  BMCBI 2006»
15 years 5 months ago
Correction of scaling mismatches in oligonucleotide microarray data
Background: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to micro...
Martino Barenco, Jaroslav Stark, Daniel Brewer, Da...
BMCBI
2005
114views more  BMCBI 2005»
15 years 5 months ago
Quality determination and the repair of poor quality spots in array experiments
Background: A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, becaus...
Brian D. M. Tom, Walter R. Gilks, Elizabeth T. Bro...
TCBB
2008
107views more  TCBB 2008»
15 years 5 months ago
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of pre...
Hyuk Cho, Inderjit S. Dhillon
CSB
2004
IEEE
164views Bioinformatics» more  CSB 2004»
15 years 8 months ago
Biclustering in Gene Expression Data by Tendency
The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data a...
Jinze Liu, Jiong Yang, Wei Wang 0010
BMEI
2009
IEEE
15 years 6 months ago
An Improved Probabilistic Model for Finding Differential Gene Expression
Abstract--Finding differentially expressed genes is a fundamental objective of a microarray experiment. Recently proposed method, PPLR, considers the probe-level measurement error ...
Li Zhang, Xuejun Liu