Tumor clustering based on penalized matrix decomposition

Chun Hou Zheng; Juan Wang; Vincent To Yee Ng; Chi Keung Simon Shiu

doi:10.1109/ICBBE.2010.5517826

Tumor clustering based on penalized matrix decomposition

Chun Hou Zheng, Juan Wang, Vincent To Yee Ng, Chi Keung Simon Shiu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

A reliable and precise identification of the type of tumors is essential for effective treatment of cancer. In this paper, we proposed a novel method to cluster tumors using gene expression data. In this method, we use penalized matrix decomposition (PMD) to extract metasamples from gene expression data. Specially, a metasample can capture structures inherent in the samples in one class. In addition, we present how to use the factors of PMD to cluster the samples. Compared with traditional methods, such as HC, SOM and NMF etc., our method can find the samples with complex classes. At the same time, the number of clusters can be determined automatically.

Original language	English
Title of host publication	2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
DOIs	https://doi.org/10.1109/ICBBE.2010.5517826
Publication status	Published - 6 Sept 2010
Event	4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 - Chengdu, China Duration: 18 Jun 2010 → 20 Jun 2010

Conference

Conference	4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
Country/Territory	China
City	Chengdu
Period	18/06/10 → 20/06/10

Keywords

Gene expression data
Penalized matrix decomposition
Tumor cluster

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.1109/ICBBE.2010.5517826

Cite this

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title = "Tumor clustering based on penalized matrix decomposition",

abstract = "A reliable and precise identification of the type of tumors is essential for effective treatment of cancer. In this paper, we proposed a novel method to cluster tumors using gene expression data. In this method, we use penalized matrix decomposition (PMD) to extract metasamples from gene expression data. Specially, a metasample can capture structures inherent in the samples in one class. In addition, we present how to use the factors of PMD to cluster the samples. Compared with traditional methods, such as HC, SOM and NMF etc., our method can find the samples with complex classes. At the same time, the number of clusters can be determined automatically.",

keywords = "Gene expression data, Penalized matrix decomposition, Tumor cluster",

author = "Zheng, {Chun Hou} and Juan Wang and Ng, {Vincent To Yee} and Shiu, {Chi Keung Simon}",

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language = "English",

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Tumor clustering based on penalized matrix decomposition. / Zheng, Chun Hou; Wang, Juan; Ng, Vincent To Yee et al.
2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010. 2010. 5517826.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Tumor clustering based on penalized matrix decomposition

AU - Zheng, Chun Hou

AU - Wang, Juan

AU - Ng, Vincent To Yee

AU - Shiu, Chi Keung Simon

PY - 2010/9/6

Y1 - 2010/9/6

N2 - A reliable and precise identification of the type of tumors is essential for effective treatment of cancer. In this paper, we proposed a novel method to cluster tumors using gene expression data. In this method, we use penalized matrix decomposition (PMD) to extract metasamples from gene expression data. Specially, a metasample can capture structures inherent in the samples in one class. In addition, we present how to use the factors of PMD to cluster the samples. Compared with traditional methods, such as HC, SOM and NMF etc., our method can find the samples with complex classes. At the same time, the number of clusters can be determined automatically.

AB - A reliable and precise identification of the type of tumors is essential for effective treatment of cancer. In this paper, we proposed a novel method to cluster tumors using gene expression data. In this method, we use penalized matrix decomposition (PMD) to extract metasamples from gene expression data. Specially, a metasample can capture structures inherent in the samples in one class. In addition, we present how to use the factors of PMD to cluster the samples. Compared with traditional methods, such as HC, SOM and NMF etc., our method can find the samples with complex classes. At the same time, the number of clusters can be determined automatically.

KW - Gene expression data

KW - Penalized matrix decomposition

KW - Tumor cluster

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U2 - 10.1109/ICBBE.2010.5517826

DO - 10.1109/ICBBE.2010.5517826

M3 - Conference article published in proceeding or book

SN - 9781424447138

BT - 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010

T2 - 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010

Y2 - 18 June 2010 through 20 June 2010

ER -

Tumor clustering based on penalized matrix decomposition

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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