article

Molecular Pattern Discovery Based on Penalized Matrix Decomposition

Authors:

Chun-Hou Zheng,

Vincent To-Yee Ng,

Chi Keung Shiu,

D. -S. HuangAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 8, Issue 6

Pages 1592 - 1603

https://doi.org/10.1109/TCBB.2011.79

Published: 01 November 2011 Publication History

Abstract

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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Molecular Pattern Discovery Based on Penalized Matrix Decomposition

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 8, Issue 6

November 2011

286 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 November 2011

Published in TCBB Volume 8, Issue 6

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https://dl.acm.org/doi/10.1155/2021/9990297
Su XYou ZChen ZYi HGuo Z(2021)Protein-Protein Interaction Prediction by Integrating Sequence Information and Heterogeneous Network RepresentationIntelligent Computing Theories and Application10.1007/978-3-030-84532-2_55(617-626)Online publication date: 12-Aug-2021
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