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DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with
Zhenyu Weng1, Zongliang Yue2, Yuesheng Zhu1
1Communication and Information Security Lab, Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, Shenzhen 518055, China.
Bioinformatics (Oxford, England)
|June 27, 2022
Summary
A new algorithm, DEMA, enhances biological network visualization by integrating gene properties directly into graph layout. This improves the interpretation of complex networks for disease gene discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Graph layout algorithms are crucial for visualizing biological networks and revealing cellular contexts.
- Current layout engines often process biological properties after initial layout, limiting interpretability.
- Integrating biological factors during layout can enhance the understanding of complex biological networks.
Purpose of the Study:
- To introduce a novel layout algorithm, the distance-bounded energy-field minimization algorithm (DEMA), designed to natively incorporate biological properties.
- To improve the interpretation of complex biological network graphs by considering gene-specific and network-wide factors.
- To facilitate gene candidate discovery in complex diseases through enhanced network visualization.
Main Methods:
- Developed DEMA, a layout algorithm utilizing a parameterized energy model with nodes repelled by topology and attracted by biological factors.
- Generalized biological factors into gene weights, protein-protein interaction weights, gene-to-gene correlations, and gene set annotations.
- Implemented DEMA as a Cytoscape plugin for convenient network visualization and analysis.
Main Results:
- DEMA successfully integrates biological properties like gene association strength and functional groups into the layout process.
- Case studies using genetic data from autism spectrum disorder and Alzheimer's disease demonstrated DEMA's utility in gene candidate discovery.
- The algorithm allows for adjustable attraction/repulsion/grouping coefficients to customize network views.
Conclusions:
- DEMA offers a significant advancement in biological network visualization by embedding biological context directly into the layout.
- The algorithm enhances the interpretability of complex networks, aiding in the discovery of disease-related genes.
- DEMA provides a flexible and convenient tool for researchers in bioinformatics and computational biology.
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