Microarray data analysis using rival penalized em algorithm in normal mixture models

Xing Ming Zhao*, Yiu Ming CHEUNG, De Shuang Huang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Microarray technology is a useful tool for monitoring the expressed levels of thousands of genes simultaneously. Recently, mixture modelling has been used to extract information from expressed genes. It utilizes two separate steps to estimate the number of classes and model parameters, respectively, which however may be time-consuming and fall into sub-optimal solutions. In this paper, we therefore apply an one-step approach, namely Rival Penalized Expectation-Maximization (RPEM) algorithm, to microarray data analysis. The RPEM algorithm is capable of estimating the parameters of the normal mixture model, meanwhile determining the number of classes automatically. The numerical results have shown the effectiveness of this technique on real microarray data analysis.

Original languageEnglish
Title of host publicationProceedings of the 2005 IEEE International Workshop on VLSI Design and Video Technology, IWVDVT 2005
Pages35-38
Number of pages4
Publication statusPublished - 2005
Event2005 IEEE International Workshop on VLSI Design and Video Technology, IWVDVT 2005 - Suzhou, China
Duration: 28 May 200530 May 2005

Publication series

NameProceedings of the 2005 IEEE International Workshop on VLSI Design and Video Technology, IWVDVT 2005

Conference

Conference2005 IEEE International Workshop on VLSI Design and Video Technology, IWVDVT 2005
Country/TerritoryChina
CitySuzhou
Period28/05/0530/05/05

Scopus Subject Areas

  • Engineering(all)

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