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» Evaluation of clustering algorithms for gene expression data
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BMCBI
2006
200views more  BMCBI 2006»
15 years 1 months ago
Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data
Background: Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, clas...
Ian B. Jeffery, Desmond G. Higgins, Aedín C...
JBI
2006
107views Bioinformatics» more  JBI 2006»
15 years 1 months ago
Knowledge guided analysis of microarray data
To microarray expression data analysis, it is well accepted that biological knowledge-guided clustering techniques show more advantages than pure mathematical techniques. In this ...
Zhuo Fang, Jiong Yang, Yixue Li, Qing-ming Luo, Le...
BMCBI
2005
110views more  BMCBI 2005»
15 years 1 months ago
Considerations when using the significance analysis of microarrays (SAM) algorithm
Background: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments...
Ola Larsson, Claes Wahlestedt, James A. Timmons
BMCBI
2008
166views more  BMCBI 2008»
15 years 1 months ago
Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies
Background: The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem...
Peter A. DiMaggio Jr., Scott R. McAllister, Christ...
BMCBI
2007
126views more  BMCBI 2007»
15 years 1 months ago
Including probe-level uncertainty in model-based gene expression clustering
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...