Jesse D Ziebarth1, Anindya Bhattacharya, Yan Cui. 1. Department of Microbiology, Immunology and Biochemistry and Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Abstract
SUMMARY: The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. AVAILABILITY AND IMPLEMENTATION: BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). CONTACT: ycui2@uthsc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. AVAILABILITY AND IMPLEMENTATION: BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). CONTACT: ycui2@uthsc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: A K Cashion; D K Hathaway; A Stanfill; F Thomas; J D Ziebarth; Y Cui; P A Cowan; J Eason Journal: Clin Transplant Date: 2014-11 Impact factor: 2.863
Authors: Fuyi Xu; Yuanjian Chen; Kaitlin A Tillman; Yan Cui; Robert W Williams; Syamal K Bhattacharya; Lu Lu; Yao Sun Journal: Int J Cardiol Date: 2021-01-30 Impact factor: 4.164
Authors: Robert Šket; Tadej Debevec; Susanne Kublik; Michael Schloter; Anne Schoeller; Boštjan Murovec; Katarina Vogel Mikuš; Damjan Makuc; Klemen Pečnik; Janez Plavec; Igor B Mekjavić; Ola Eiken; Zala Prevoršek; Blaž Stres Journal: Front Physiol Date: 2018-03-13 Impact factor: 4.566