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KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 5 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
BMCBI
2005
110views more  BMCBI 2005»
13 years 5 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
FLAIRS
2004
13 years 6 months ago
Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm
One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. D...
ZhenQiu Liu, Dechang Chen
BMCBI
2008
145views more  BMCBI 2008»
13 years 5 months ago
Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis
Background: The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, ...
Shameek Biswas, John D. Storey, Joshua M. Akey
ICDM
2002
IEEE
158views Data Mining» more  ICDM 2002»
13 years 10 months ago
Adaptive dimension reduction for clustering high dimensional data
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst...