A novel algorithm for generating simulated genetic data based on K-medoids

Author

Jianan Wu ; Chunguang Zhou ; Zhangxu Li ; Xuefei Xia ; Seng Zhang ; You Zhou

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China

fYear

2012

fDate

Oct. 30 2012-Nov. 1 2012

Firstpage

25

Lastpage

28

Abstract

Genetic data is very important for biological research, but it is hard to be obtained by experiment. In this paper, we introduce an algorithm for generating simulated genetic data based on K-mediods. A concept of Cluster Channel is proposed in this algorithm and used to generate simulated data. The noise of origin data could be eliminated using the proposed method. The experimental results show reliability of simulated genetic data. SAM is used to analyze the simulated data and original data, and we get a conclusion that the simulated data can effectively validate differentially expressed gene detected algorithm.

Keywords

biology; data handling; genetic algorithms; pattern clustering; K-medoids; biological research; cluster channel concept; gene detected algorithm; novel algorithm; simulated genetic data; Algorithm design and analysis; Arrays; Clustering algorithms; Gene expression; Noise; Cluster channel; Genetic data; K-medoids; Simulated genetic data;

fLanguage

English

Publisher

ieee

Conference_Titel

Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-1855-6

Type

conf

DOI

10.1109/CCIS.2012.6664360

Filename

6664360