WIREs Computational Statistics

Multiscalar genetic pathway modeling with hybrid Bayesian networks

Early View

Abstract Bayesian network modeling of real world datasets is often complicated by the fact that they are hybrid datasets that contain both discrete and continuous variables. For example, recent advances in high throughput biotechnologies have made it possible to generate large‐scale data across multiple biological scales—from discrete variables such as DNA variations to continuous variables such as omics traits and disease phenotypes. Such large heterogeneous and multiscalar datasets present a great challenge for biological knowledge discovery. Here we discuss the Bayesian Network Webserver (http://compbio.uthsc.edu/BNW), a web‐based platform for creating hybrid Bayesian network models, and its use in discovering causal relationships from heterogeneous and multiscalar system genetics datasets. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Models > Bayesian Models Statistical Models > Graphical Models Applications of Computational Statistics > Computational and Molecular Biology

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