NDLI: Partial Mixture Model for Tight Clustering in Exploratory Gene Expression Analysis

Content Provider	IEEE Xplore Digital Library
Author	Yinyin Yuan Chang-Tsun Li
Copyright Year	2007
Description	Author affiliation: Warwick Univ., Coventry (Yinyin Yuan; Chang-Tsun Li)
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.
Starting Page	1061
Ending Page	1065
File Size	1415756
Page Count	5
File Format	PDF
ISBN	9781424415090
DOI	10.1109/BIBE.2007.4375689
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Maximum likelihood estimation Model driven engineering Clustering algorithms Scattering Biological system modeling Spline Robustness Data analysis Computer science
Content Type	Text
Resource Type	Article

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