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Bayesian network feature finder (BANFF): an R package for gene network feature selection

Lan, Zhou, Zhao, Yize, Kang, Jian, Yu, Tianwei
Bioinformatics 2016 v.32 no.23 pp. 3685-3687
Bayesian theory, Markov chain, algorithms, bioinformatics, computer software, gene expression, genes, models, protein-protein interactions
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: Contact:, Supplementary information: Supplementary data are available at Bioinformatics online.