Evolutionary Correlation Triclustering for 3D Gene Expression Data

Author(s):  
N. Narmadha ◽  
R. Rathipriya

At present, triclustering is the well known data mining technique for analysis of 3D gene expression data (GST). Triclustering is a simultaneously clustering of subset of Gene (G), subset of Sample (S), and over a subset of Time point (T). Triclustering approach identifies a coherent pattern in the 3D gene expression data using Mean Correlation Value (MCV). In this chapter, Hybrid PSO based algorithm is developed for triclustering of 3D gene expression data. This algorithm can effectively find the coherent pattern with high volume of a tricluster. The experimental study is conducted on yeast cycle dataset to study the biological significance of the coherent tricluster using gene ontology tool


2007 ◽  
Author(s):  
E. Wes Bethel ◽  
Oliver Rubel ◽  
Gunther H. Weber ◽  
Bernd Hamann ◽  
Hans Hagen

Author(s):  
O. Rubel ◽  
G.H. Weber ◽  
Min-Yu Huang ◽  
E.W. Bethel ◽  
M.D. Biggin ◽  
...  

Author(s):  
Oliver Rübel ◽  
Soile V. E. Keränen ◽  
Mark Biggin ◽  
David W. Knowles ◽  
Gunther H. Weber ◽  
...  

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