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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Guide to Conquer the Biological Network Era Using Graph Theory.

Mikaela Koutrouli1, Evangelos Karatzas1,2, David Paez-Espino3

  • 1Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.

Frontiers in Bioengineering and Biotechnology
|February 22, 2020
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Summary
This summary is machine-generated.

This review covers graph theory basics for biological networks, detailing data structures, properties, and analysis algorithms. It aims to guide researchers in understanding and analyzing complex biological systems.

Keywords:
biological networksclusteringgraph theorytopologyvisualization

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological systems are commonly represented as networks of interacting bioentities.
  • Understanding these complex networks is crucial for biological and biomedical research.

Purpose of the Study:

  • To provide a comprehensive overview of graph theory concepts relevant to biological networks.
  • To discuss data structures, properties, and analysis methods for biological networks.
  • To highlight tools and algorithms for network analysis.

Main Methods:

  • Review of graph theory fundamentals.
  • Description of network properties and topological features.
  • Discussion of biological network types and file formats.
  • Overview of network analysis algorithms and tools.

Main Results:

  • Detailed explanation of graph theory concepts and data structures.
  • Categorization of biological network types and their formats.
  • Introduction to various network analysis algorithms and metrics.
  • Highlighting state-of-the-art tools for network analysis.

Conclusions:

  • This review serves as a foundational resource for analyzing biological networks.
  • It bridges the gap between graph theory and its application in biology.
  • Encourages further exploration and advancement in the field of network biology.