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» A Repulsive Clustering Algorithm for Gene Expression Data
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BMCBI
2005
112views more  BMCBI 2005»
13 years 7 months ago
Visualization methods for statistical analysis of microarray clusters
Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determin...
Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Ol...
SDM
2007
SIAM
98views Data Mining» more  SDM 2007»
13 years 9 months ago
Lattice based Clustering of Temporal Gene-Expression Matrices
Individuals show different cell classes when they are in the different stages of a disease, have different disease subtypes, or have different response to a treatment or envir...
Yang Huang, Martin Farach-Colton
BMCBI
2006
202views more  BMCBI 2006»
13 years 7 months ago
Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
Background: The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the sea...
David J. Reiss, Nitin S. Baliga, Richard Bonneau
IADIS
2008
13 years 9 months ago
Mib: Using Mutual Information for Biclustering High Dimensional Data
Most of the biclustering algorithms for gene expression data are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However...
Neelima Gupta, Seema Aggarwal
PR
2006
116views more  PR 2006»
13 years 7 months ago
Shared farthest neighbor approach to clustering of high dimensionality, low cardinality data
Clustering algorithms are routinely used in biomedical disciplines, and are a basic tool in bioinformatics. Depending on the task at hand, there are two most popular options, the ...
Stefano Rovetta, Francesco Masulli