Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Summary: The Bayesian Network bserver (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 beten 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.  suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW).
['Jesse D. Ziebarth', 'Anindya Bhattacharya', 'Yan Cui']
I am writing a research paper on a idea Bayesian Network Webserver: a comprehensive tool for biological network modeling
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian Network Webserver: a comprehensive tool for biological network modeling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Summary: The Bayesian Network bserver (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 beten 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.  suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW)."}]