Partial mixture model for tight clustering in exploratory gene expression analysis

Yinyin Yuan, Chang Tsun Li

Research output: Book chapter/Published conference paperConference paperpeer-review

2 Citations (Scopus)

Abstract

In this paper we demonstrate the inherent robustness of minimum distance estimator that makes it a potentially powerful tool for parameter estimation in gene expression time course analysis. To apply minimum distance estimator to gene expression clustering, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated specially for tight clustering. Recently tight clustering was proposed as a response for obtaining tighter and thus more informative clusters in gene expression studies. We provide interesting results through data fitting when compared with maximum likelihood estimator using simulated data. The experiments on real gene expression data validated our proposed partial regression clustering algorithm. Our aim is to provide interpretations, discussions and examples that serve as resources for future research.

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages1061-1065
Number of pages5
DOIs
Publication statusPublished - 2007
Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
Duration: 14 Jan 200717 Jan 2007

Conference

Conference7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
CountryUnited States
CityBoston, MA
Period14/01/0717/01/07

Fingerprint Dive into the research topics of 'Partial mixture model for tight clustering in exploratory gene expression analysis'. Together they form a unique fingerprint.

Cite this