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Molecular Pattern Discovery Based on Penalized Matrix Decomposition
November/December 2011 (vol. 8 no. 6)
pp. 1592-1603
ASCII Text
x 
 
Chun-Hou Zheng, Lei Zhang, Vincent To-Yee Ng, Simon Chi-Keung Shiu, De-Shuang Huang, "Molecular Pattern Discovery Based on Penalized Matrix Decomposition," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1592-1603, November/December, 2011.
 
 
BibTex
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@article{ 10.1109/TCBB.2011.79,
author = {Chun-Hou Zheng and Lei Zhang and Vincent To-Yee Ng and Simon Chi-Keung Shiu and De-Shuang Huang},
title = {Molecular Pattern Discovery Based on Penalized Matrix Decomposition},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {8},
number = {6},
issn = {1545-5963},
year = {2011},
pages = {1592-1603},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.79},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Molecular Pattern Discovery Based on Penalized Matrix Decomposition
IS - 6
SN - 1545-5963
SP1592
EP1603
EPD - 1592-1603
A1 - Chun-Hou Zheng,
A1 - Lei Zhang,
A1 - Vincent To-Yee Ng,
A1 - Simon Chi-Keung Shiu,
A1 - De-Shuang Huang,
PY - 2011
KW - Tumor clustering
KW - penalized matrix decomposition
KW - metasample
KW - gene expression data
KW - developmental biology.
VL - 8
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Chun-Hou Zheng, The Hong Kong Polytechnic University, Hong Kong and Qufu Normal University, Rizhao, Shandong
Lei Zhang, The Hong Kong Polytechnic University, Hong Kong
Vincent To-Yee Ng, The Hong Kong Polytechnic University, Hong Kong
Simon Chi-Keung Shiu, The Hong Kong Polytechnic University, Hong Kong
De-Shuang Huang, Tongji University, Shanghai
A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.

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Index Terms:
Tumor clustering, penalized matrix decomposition, metasample, gene expression data, developmental biology.
Citation:
Chun-Hou Zheng, Lei Zhang, Vincent To-Yee Ng, Simon Chi-Keung Shiu, De-Shuang Huang, "Molecular Pattern Discovery Based on Penalized Matrix Decomposition," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1592-1603, Nov.-Dec. 2011, doi:10.1109/TCBB.2011.79
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