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