Partial mixture model for tight clustering in exploratory gene expression analysis
Yuan, Yinyin, Li, Chang-Tsun and Yang, JY (2007) Partial mixture model for tight clustering in exploratory gene expression analysis. In: 7th IEEE International Conference on Bioinformatics and Bioengineering, Boston, MA, OCT 14-17, 2007. Published in: PROCEEDINGS OF THE 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, VOLS I AND II pp. 1061-1065.Full text not available from this repository.
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.
|Item Type:||Conference Item (UNSPECIFIED)|
Q Science > QH Natural history > QH301 Biology
|Journal or Publication Title:||PROCEEDINGS OF THE 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, VOLS I AND II|
|Editor:||Yang, MQ and Zhu, MM and Zhang, Y and Arabnia, HR and Deng, Y and Bourbakis, N|
|Number of Pages:||5|
|Page Range:||pp. 1061-1065|
|Title of Event:||7th IEEE International Conference on Bioinformatics and Bioengineering|
|Location of Event:||Boston, MA|
|Date(s) of Event:||OCT 14-17, 2007|
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