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BMCBI
2006
183views more  BMCBI 2006»
14 years 9 months ago
Mining gene expression data by interpreting principal components
Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many insta...
Joseph C. Roden, Brandon W. King, Diane Trout, Ali...
85
Voted
BMCBI
2008
142views more  BMCBI 2008»
14 years 9 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
BMCBI
2006
170views more  BMCBI 2006»
14 years 9 months ago
Biclustering of gene expression data by non-smooth non-negative matrix factorization
Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of gene...
Pedro Carmona-Saez, Roberto D. Pascual-Marqui, Fra...
SAC
2006
ACM
15 years 3 months ago
Two-phase clustering strategy for gene expression data sets
In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and con...
Dirk Habich, Thomas Wächter, Wolfgang Lehner,...
92
Voted
BMCBI
2008
146views more  BMCBI 2008»
14 years 9 months ago
A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data
Background: The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a nu...
Chang Sik Kim, Cheol Soo Bae, Hong Joon Tcha