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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (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 languageEnglish
Title of host publication2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
DOIs
Publication statusPublished - 6 Sep 2010
Event4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 - Chengdu, China
Duration: 18 Jun 201020 Jun 2010

Conference

Conference4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
Country/TerritoryChina
CityChengdu
Period18/06/1020/06/10

Keywords

  • Gene expression data
  • Penalized matrix decomposition
  • Tumor cluster

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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