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Pairwise Biological Network Alignment Based on Discrete Bat Algorithm.

Jing Chen1,2, Ying Zhang1, Jin-Fang Xia1

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China.

Computational and Mathematical Methods in Medicine
|November 15, 2021
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Summary
This summary is machine-generated.

This study introduces BatAlign, a novel network alignment algorithm inspired by the bat algorithm. BatAlign efficiently identifies conserved functional modules and evolutionary relationships in biological networks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput technologies generate vast biological network data.
  • Network alignment is crucial for identifying conserved functional modules and evolutionary relationships.
  • Efficient computational tools are needed for network alignment.

Purpose of the Study:

  • To develop an efficient computational network aligner for biological networks.
  • To apply a discretized bat algorithm for network alignment.
  • To propose the global pairwise alignment algorithm BatAlign.

Main Methods:

  • Discretization of the classic bat algorithm for network alignment.
  • BatAlign uses a discrete code for individual velocity and position.
  • Objective function based on the number of conserved edges.
  • Population initialization using network similarity.

Main Results:

  • BatAlign successfully matches proteins with high functional consistency.
  • The algorithm achieves relatively high topological quality in network alignment.
  • Experimental results demonstrate the effectiveness of BatAlign.

Conclusions:

  • BatAlign offers an efficient approach to biological network alignment.
  • The proposed method aids in understanding evolutionary relationships across species.
  • BatAlign contributes to the analysis of conserved functional modules in biological networks.