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

Shuanhu Wu; Alan Wee Chung Liew; Hong Yan

doi:10.1007/11427469_113

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

Shuanhu Wu, Alan Wee Chung Liew, Hong Yan

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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. © Springer-Verlag Berlin Heidelberg 2005.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2005
Subtitle of host publication	Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part III
Editors	Jun Wang, Xiao-Feng Liao, Zhang Yi
Publisher	Springer
Pages	709-718
ISBN (Electronic)	978-3-540-32069-2
ISBN (Print)	978-3-540-25914-5
DOIs	https://doi.org/10.1007/11427469_113
Publication status	Published - 2005
Event	2nd International Symposium on Neural Networks (ISNN 2005) - Chongqing, China Duration: 30 May 2005 → 1 Jun 2005

Publication series

Name	Lecture Notes in Computer Science
Volume	3498
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Symposium on Neural Networks (ISNN 2005)
Place	China
City	Chongqing
Period	30/05/05 → 1/06/05

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Wu, S., Liew, A. W. C., & Yan, H. (2005). OPTOC-based clustering analysis of gene expression profiles in spectral space. In J. Wang, X.-F. Liao, & Z. Yi (Eds.), Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part III (pp. 709-718). (Lecture Notes in Computer Science; Vol. 3498). Springer . https://doi.org/10.1007/11427469_113

Wu, Shuanhu ; Liew, Alan Wee Chung ; Yan, Hong. / OPTOC-based clustering analysis of gene expression profiles in spectral space. Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part III. editor / Jun Wang ; Xiao-Feng Liao ; Zhang Yi. Springer , 2005. pp. 709-718 (Lecture Notes in Computer Science).

@inproceedings{9b76f80f131440f3a6fc4d446fc855cb,

title = "OPTOC-based clustering analysis of gene expression profiles in spectral space",

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. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.",

author = "Shuanhu Wu and Liew, {Alan Wee Chung} and Hong Yan",

year = "2005",

doi = "10.1007/11427469_113",

language = "English",

isbn = "978-3-540-25914-5",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "709--718",

editor = "Jun Wang and Xiao-Feng Liao and Zhang Yi",

booktitle = "Advances in Neural Networks - ISNN 2005",

note = "2nd International Symposium on Neural Networks (ISNN 2005) ; Conference date: 30-05-2005 Through 01-06-2005",

}

Wu, S, Liew, AWC & Yan, H 2005, OPTOC-based clustering analysis of gene expression profiles in spectral space. in J Wang, X-F Liao & Z Yi (eds), Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part III. Lecture Notes in Computer Science, vol. 3498, Springer , pp. 709-718, 2nd International Symposium on Neural Networks (ISNN 2005), Chongqing, China, 30/05/05. https://doi.org/10.1007/11427469_113

OPTOC-based clustering analysis of gene expression profiles in spectral space. / Wu, Shuanhu; Liew, Alan Wee Chung; Yan, Hong.
Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part III. ed. / Jun Wang; Xiao-Feng Liao; Zhang Yi. Springer , 2005. p. 709-718 (Lecture Notes in Computer Science; Vol. 3498).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - OPTOC-based clustering analysis of gene expression profiles in spectral space

AU - Wu, Shuanhu

AU - Liew, Alan Wee Chung

AU - Yan, Hong

PY - 2005

Y1 - 2005

N2 - 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. © Springer-Verlag Berlin Heidelberg 2005.

AB - 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. © Springer-Verlag Berlin Heidelberg 2005.

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U2 - 10.1007/11427469_113

DO - 10.1007/11427469_113

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-540-25914-5

T3 - Lecture Notes in Computer Science

SP - 709

EP - 718

BT - Advances in Neural Networks - ISNN 2005

A2 - Wang, Jun

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T2 - 2nd International Symposium on Neural Networks (ISNN 2005)

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ER -

Wu S, Liew AWC, Yan H. OPTOC-based clustering analysis of gene expression profiles in spectral space. In Wang J, Liao XF, Yi Z, editors, Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part III. Springer . 2005. p. 709-718. (Lecture Notes in Computer Science). Epub 2005 May 4. doi: 10.1007/11427469_113

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

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