title: Model-based Bayesian clustering (MBBC).
abstract: BACKGROUND
  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 scientists, and is built with three free computer language software packages: Ox, R and C++, taking advantage of the strengths of each language. Within MBBC, the search algorithm is implemented with Ox and resulting graphs are drawn with R. A user-friendly graphical interface is built with C++ to run the Ox and R programs internally. Thus, MBBC users are not required to know how to use Ox, R or C++, but they must be pre-installed.
  
  
  BACKGROUND
  A self-extractable zip file, MBBC20zip.exe, is available at the MBBC webpage www.stat.ufl.edu/~casella/mbbc/, which contains MBBC.exe, source files, and all other related files. The current version works only in the Windows operating system. A free installation program and overview for Ox is available at www.doornik.com. A detailed installation guide for Ox is provided by MBBC, and is accessible without installing Ox. R is available at www.r-project.org/.
authors.affiliation: Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Yongsung
authors.initials: Y
authors.lastname: Joo
authors.forename: James G
authors.initials: JG
authors.lastname: Booth
authors.forename: Younghwan
authors.initials: Y
authors.lastname: Namkoong
authors.forename: George
authors.initials: G
authors.lastname: Casella
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*