Year: 2016
Type: article
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: [email protected], [email protected] information: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Zhou Lan, Yize Zhao, Jian Kang, Tianwei Yu
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W2510258558