OPTOC-based clustering analysis of gene expression profiles in spectral space 

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Title OPTOC-based clustering analysis of gene expression profiles in spectral space 
Author Wu, Shuanhu; Liew, Alan Wee-Chung; Yan, Hong
Publication Title Advances in Neural Networks - ISNN 2005
Editor Jun Wang
Year Published 2005
Place of publication Berlin
Publisher Springer
Abstract In this paper, a new feature extracting method and clustering scheme in spectral space for gene expression data was proposed. We model each member of same cluster as the sum of cluster's representative term and experimental artifacts term. More compact clusters and hence better clustering results can be obtained through extracting essential features or reducing experimental artifacts. In term of the periodicity of gene expression profile data, features extracting is performed in DCT domain by soft-thresholding de-noising method. Clustering process is based on OPTOC competitive learning strategy. The results for clustering real gene expression profiles show that our method is better than directly clustering in the original space.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1007/11427469_113
ISBN 3-540-25912-0
Conference name 2nd International Symposium on Neural Networks
Location Chongqing, China
Date From 2005-05-30
Date To 2005-06-01
URI http://hdl.handle.net/10072/24589
Date Accessioned 2009-06-10
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Gene Expression
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1x

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