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» Evaluation of clustering algorithms for gene expression data
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BMCBI
2006
136views more  BMCBI 2006»
14 years 9 months ago
Metric for Measuring the Effectiveness of Clustering of DNA Microarray Expression
Background: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultane...
Raja Loganantharaj, Satish Cheepala, John Clifford
BMCBI
2008
167views more  BMCBI 2008»
14 years 9 months ago
Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classificat
Background: Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of ide...
Michael Gormley, Aydin Tozeren
IJCNN
2008
IEEE
15 years 3 months ago
Comparative study on normalization procedures for cluster analysis of gene expression datasets
—Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attribute...
Marcílio Carlos Pereira de Souto, Daniel S....
SDM
2007
SIAM
98views Data Mining» more  SDM 2007»
14 years 11 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
ICPR
2006
IEEE
15 years 10 months ago
Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning
Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may ...
Rave Harpaz, Robert M. Haralick