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Methods Mol Biol. 2017;1488:319-335.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

Author information

1
Department of Microbiology, Immunology and Biochemistry, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
2
Department of Microbiology, Immunology and Biochemistry, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA. ycui2@uthsc.edu.

Abstract

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

KEYWORDS:

Bayesian network modeling; Causal network; Prediction; Probabilistic inference; Webserver

PMID:
27933532
DOI:
10.1007/978-1-4939-6427-7_15
[Indexed for MEDLINE]

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