Zhou Lan1, Yize Zhao2, Jian Kang3, Tianwei Yu4. 1. Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA. 2. Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY 10065, USA. 3. Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA. 4. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
Abstract
MOTIVATION: Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. RESULTS: We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network. AVAILABILITY: https://cran.r-project.org/web/packages/BANFF/index.html CONTACT: jiankang@umich.edu, tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
MOTIVATION: Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. RESULTS: We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network. AVAILABILITY: https://cran.r-project.org/web/packages/BANFF/index.html CONTACT: jiankang@umich.edu, tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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