KDDN: an open-source Cytoscape app for constructing differential dependency networks with significant rewiring
Ye Tian1, Bai Zhang1, Eric P Hoffman1
1Department of Electrical & Computer Engineering, Virginia Tech, Arlington, VA 22203, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057 and Department of Medicine, Wake Forest University, Winston-Salem, NC 27157, USA.
Bioinformatics (Oxford, England)
|October 3, 2014
Summary
We developed a new method to build molecular networks, identifying significant rewiring. This tool integrates biological knowledge with data for efficient network analysis and discovery.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Molecular network analysis is crucial for understanding biological systems.
- Identifying differential networks and gene rewiring provides insights into dynamic biological processes.
- Existing methods may lack the ability to integrate prior biological knowledge effectively.
Purpose of the Study:
- To develop an integrated molecular network learning method for constructing differential dependency networks.
- To create a user-friendly tool that optimally integrates prior biological knowledge with measured data.
- To enable the simultaneous construction of common and differential networks with quantitative parameter assignment.
Main Methods:
- Developed an integrated molecular network learning method within a mathematical framework.
- Implemented the knowledge-fused differential dependency networks (KDDN) method as a Java Cytoscape app.
- Utilized parallel computing for computational efficiency on multi-core machines.
Main Results:
- Successfully constructed differential dependency networks with significant rewiring.
- Demonstrated KDDN's performance on simulations and real gene expression datasets.
- Achieved biologically plausible results, providing new insights into network rewiring.
Conclusions:
- KDDN efficiently and correctly detects network rewiring as a mechanistic principle.
- The methodology is applicable to various quantitative molecular profiling data, including gene expression.
- KDDN offers a powerful approach for integrating biological knowledge and data for network analysis.
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