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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Related Experiment Video

Updated: Feb 10, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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EClerize: A customized force-directed graph drawing algorithm for biological graphs with EC attributes.

Hasan Fehmi Danaci1, Rengul Cetin-Atalay2, Volkan Atalay1

  • 1† Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.

Journal of Bioinformatics and Computational Biology
|May 23, 2018
PubMed
Summary
This summary is machine-generated.

EClerize visualizes biological pathways by clustering nodes with similar Enzyme Commission (EC) attributes. This algorithm enhances biological graph analysis by optimizing node positions for clarity and understanding.

Keywords:
Biological graphCytoscapeEC numberenzymenetwork visualization

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Visualizing large-scale biological data from high-throughput experiments is crucial for understanding complex biological systems.
  • Biological pathways are often represented as graphs, but effective visualization techniques are needed for improved analysis.

Purpose of the Study:

  • To introduce EClerize, a novel force-directed layout algorithm for biological graphs.
  • To improve the visualization of biological pathways by incorporating Enzyme Commission (EC) attributes for node clustering.

Main Methods:

  • Developed a customized force-directed layout algorithm, EClerize.
  • Grouped nodes with identical EC class numbers into clusters.
  • Determined node positions based on biological similarity and network connectivity.
  • Optimized layout by minimizing intra-cluster distances and maximizing inter-cluster distances.

Main Results:

  • EClerize effectively clusters nodes based on EC attributes, enhancing biological graph interpretability.
  • The algorithm improves upon existing methods by considering both biological similarity and network structure.
  • Demonstrated improved visualization of biological pathways through case studies.

Conclusions:

  • EClerize offers a powerful new approach for visualizing and analyzing biological pathway data.
  • The algorithm facilitates a deeper understanding of complex biological networks by leveraging EC classifications.
  • EClerize is available as a Cytoscape plugin, promoting its adoption in biological research.