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Application of Multilayer Network Models in Bioinformatics.

Yuanyuan Lv1,2, Shan Huang3, Tianjiao Zhang4

  • 1Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China.

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|April 19, 2021
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Summary
This summary is machine-generated.

Multilayer networks are powerful tools for analyzing complex biological systems. This review details their applications in bioinformatics, from cells to complex brain research, highlighting recent advancements.

Keywords:
bioinformaticsbiological systemsbrain network structurechronological networksmultilayer networks

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Area of Science:

  • Bioinformatics and Network Science
  • Computational Biology
  • Systems Biology

Background:

  • The increasing complexity of biological data necessitates advanced analytical tools.
  • Multilayer networks offer a robust framework for modeling intricate biological systems.
  • Bioinformatics has seen a surge in network-based approaches.

Purpose of the Study:

  • To review and classify the applications of multilayer network models in bioinformatics.
  • To summarize the latest advancements in this interdisciplinary field.
  • To provide a hierarchical categorization of biological systems studied using these networks.

Main Methods:

  • Classification of multilayer network applications based on the object of study.
  • Hierarchical categorization of biological subjects (cells, tissues, organs, groups).
  • Detailed case study on brain research, including chronological networks.

Main Results:

  • A structured overview of how multilayer networks are applied across various biological scales.
  • Demonstration of multilayer and chronological network utility in brain research.
  • Identification of key trends and results in multilayer network bioinformatics.

Conclusions:

  • Multilayer networks are essential for understanding complex biological systems.
  • The hierarchical classification provides a framework for future research.
  • Network quality assessment methods are crucial for evaluating biological network studies.