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Related Experiment Video

Updated: May 8, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

Jesse D Ziebarth¹, Anindya Bhattacharya, Yan Cui

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

Bioinformatics (Oxford, England)

|August 24, 2013

Summary

This summary is machine-generated.

The Bayesian Network Webserver (BNW) provides a platform for comprehensive network modeling of biological data, enabling users to analyze complex relationships in systems genetics. BNW supports hybrid datasets and incorporates prior knowledge for robust network structure learning and hypothesis generation.

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Last Updated: May 8, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Computational biology
Systems genetics
Bioinformatics

Background:

Biological datasets often contain mixed discrete and continuous variables.
Understanding relationships between variables is crucial in systems genetics.
Existing platforms may lack comprehensive network modeling capabilities for hybrid datasets.

Purpose of the Study:

Introduce the Bayesian Network Webserver (BNW) as a platform for network modeling.
Facilitate the analysis of systems genetics and other biological datasets.
Enable users to learn network structures and explore variable relationships.

Main Methods:

BNW allows seamless data uploading and network structure learning.
The platform supports hybrid datasets with discrete and continuous variables.
Users can incorporate prior knowledge via a structural constraint interface.

Main Results:

BNW generates an interactive network model after structure learning.
The platform facilitates the understanding of relationships between network variables.
Users can generate testable hypotheses from the learned network models.

Conclusions:

BNW is a comprehensive platform for network modeling of biological data.
It supports hybrid datasets and prior knowledge integration.
BNW empowers users to explore complex biological relationships and formulate hypotheses.