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.
|Title of host publication||Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE|
|Number of pages||5|
|Publication status||Published - 2007|
|Event||7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States|
Duration: 14 Jan 2007 → 17 Jan 2007
|Conference||7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE|
|Period||14/01/07 → 17/01/07|