Gene Cluster Algorithm Based on Most Similarity Tree

Authors:
Lu Xin-guo;Lin Ya-ping;Li Xiao-long;Yi Ye-qing;Cai li-jun;Wang Hai-jun
Affiliations:
Hunan University, Changsha, China;Hunan University, Changsha, China;Hunan University, Changsha, China;Hunan University, Changsha, China;Hunan University, Changsha, China;Hunan University, Changsha, China
Venue:
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Year:
2005

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Abstract

As the development of DNA array technology, largescale 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.