Microarray Biclustering with Crowding Based MOACO

Author

Liu, Junwan ; Li, Zhoujun ; Hu, Xiaohua ; Chen, Yiming

Author_Institution

Sch. of Comput. & Inf. Engeering, Central South Univ. of Forestry & Technol., China

fYear

2009

fDate

1-4 Nov. 2009

Firstpage

170

Lastpage

173

Abstract

Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. Ant colony optimization (ACO) algorithms have been shown to be effective problem solving strategies for multiple objective optimization (MOO). Multiple objective ant colony optimization (MOACO) mainly focuses on solving the multiple objective combinatorial optimization problems. This paper incorporates crowding update technology into MOACOB and proposes a novel crowding based MOACO biclustering algorithm to mine biclusters from microarray dataset. Experimental results are shown for biclustering algorithm on two real gene expression dataset.

Keywords

data mining; genetics; medical information systems; optimisation; MOACO biclustering algorithm; ant colony optimization algorithms; combinatorial optimization problems; gene expression dataset; microarray biclustering; microarray dataset; multiple objective ant colony optimization; multiple objective optimization; Agricultural engineering; Ant colony optimization; Bioinformatics; Biomedical computing; Clustering algorithms; Computer science; Educational institutions; Forestry; Information science; Performance evaluation;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine, 2009. BIBM '09. IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

978-0-7695-3885-3

Type

conf

DOI

10.1109/BIBM.2009.23

Filename

5341822