MBBC: Model-Based Bayesian Clustering
نویسندگان
چکیده
MBBC (Model-Based Bayesian Clustering) is developed to cluster longitudinal microarray data using a Bayesian objective function. This algorithm has advantages over conventional methods in that it clusters genes based on temporal changes of gene expressions and searches for the optimal number of clusters, as well as members of each cluster, without needing prior information. It is implemented using a stochastic search algorithm which avoids local maxima. Input and output for users is through R, and the main computational parts are written in C++ to achieve a high computational speed. MBBC.r and all other related files are available at http://www.phhp.ufl.edu/∼yjoo/MBBC.
منابع مشابه
Model-based Bayesian clustering (MBBC)
MOTIVATION The program MBBC 2.0 clusters time-course microarray data using a Bayesian product partition model. RESULTS The Bayesian product partition model in Booth et al. (2007) simultaneously searches for the optimal number of clusters, and assigns cluster memberships based on temporal changes of gene expressions. MBBC 2.0 to makes this method easily available for statisticians and scientis...
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تاریخ انتشار 2006