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

Authors:
Shuanhu Wu;Alan Wee Chung Liew;Hong Yan
Affiliations:
School of Computer Science & Technology, Yantai University, Yantai, Shandong, China;Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China;Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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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.