What makes new variants of SARS-CoV-2 concerning is not where they come from, but the mutations they contain

Abstract Motivation Bayesian networks (BNs) are widely used to model biological from experimental data. Many software packages exist infer BN structures, but the chance of getting trapped in local optima is a common challenge. Some recently developed Markov Chain Monte Carlo (MCMC) samplers called Neighborhood sampler (NS) and Hit-and-Run (HAR) sampler, have shown great potential substantially avoid this problem compared standard Metropolis-Hastings (MH) sampler. Results We BNMCMC for inferring visualizing BNs given datasets. This runs NS, HAR MH using discrete model. The main advantage that it exploits adaptive techniques efficiently explore space evaluate posterior probability candidate facilitate large-scale network inference. Availability implemented with C#.NET, ASP.NET, Jquery, Javascript D3.js. standalone version (BN visualization missing) available downloading at https://sourceforge.net/projects/bnmcmc/, where user-guide an example file provided simulation. A dedicated web server will be launched soon feature physics-based technique. Contact [email protected]