CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

ABSTRACT: Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal

SUBMITTER: McGeachie MJ 

PROVIDER: S-EPMC4055564 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature