DocumentCode :
3230405
Title :
Gene cluster algorithm based on most similarity tree
Author :
Xin-guo, Lu ; Ya-ping, Lin ; Xiao-long, Li ; Ye-Qing, Yi ; Li-jun, Cai ; Hai-jun, Wang
Author_Institution :
Coll. of Comput. & Commun., Hunan Univ., Changsha
fYear :
2005
fDate :
1-1 July 2005
Lastpage :
656
Abstract :
As the development of DNA array technology, large-scale DNA array expression data sets are produced. It is very important to construct the functional genome and denote the functions of unknown genes. This manuscript describes a gene cluster method based on the most similarity tree (CMST), which is a partition of equivalence groups of equivalence relation with similarity measure. The Gap statistic of similarity measure is introduced to determine the most optimal similarity measure and an optimally self-adaptive gene cluster algorithm based on CMST (OS-CMST) is proposed. The cluster method of CMST can get the global optimal clusters and the experiment results show that CMST outperform traditional cluster methods of K-means and SOM
Keywords :
DNA; biology computing; genetics; molecular biophysics; DNA array technology; K-means clustering; equivalence relation; functional genome; gap statistic; large-scale DNA array expression data; most similarity tree; optimal similarity measure; self-adaptive gene cluster algorithm; Bioinformatics; Clustering algorithms; DNA computing; Gene expression; Genomics; Graph theory; Partitioning algorithms; Set theory; Statistics; Tree graphs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
High-Performance Computing in Asia-Pacific Region, 2005. Proceedings. Eighth International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2486-9
Type :
conf
DOI :
10.1109/HPCASIA.2005.41
Filename :
1592337
Link To Document :
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