Title : 
Bicluster Analysis of Genome-Wide Gene Expression
         
        
            Author : 
Chen, Kuanchung ; Hu, Yuh-Jyh
         
        
            Author_Institution : 
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu
         
        
        
        
        
        
            Abstract : 
A number of biclustering approaches have been developed to mitigate the limitations of standard clustering algorithms. They have different problem formulation, search strategy and computational complexity. We proposed a new biclustering method based on the framework of market basket analysis in which a bicluster is described as a frequent itemset. As a feasibility test, we compared it with several standard clustering algorithms on a genome-wide yeast microarray dataset, and it showed very promising results. We later did a comparison between our approach and various current biclustering methods, following a systematic evaluation procedure recently published. The experimental results demonstrate that our new method outperforms the others
         
        
            Keywords : 
biology computing; genetics; pattern clustering; bicluster analysis; clustering algorithm; frequent itemset; genome-wide gene expression; market basket analysis; yeast microarray dataset; Algorithm design and analysis; Bioinformatics; Clustering algorithms; Computational complexity; Computer science; Educational institutions; Gene expression; Genomics; Itemsets; Standards development; biclustering; clustering; expression; microarray;
         
        
        
        
            Conference_Titel : 
Computational Intelligence and Bioinformatics and Computational Biology, 2006. CIBCB '06. 2006 IEEE Symposium on
         
        
            Conference_Location : 
Toronto, Ont.
         
        
            Print_ISBN : 
1-4244-0623-4
         
        
            Electronic_ISBN : 
1-4244-0624-2
         
        
        
            DOI : 
10.1109/CIBCB.2006.330994