Effects of Meta-Heuristic Algorithms in Protein-Protein Interaction Networks Alignment in Five Biological Species

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The biological knowledge of different species can be transferred to the conserved sequence regions via genomic sequence alignment. Similarly, through biological network alignment, knowledge of the conserved regions of molecular networks can be transferred to different conserved regions of different species. Therefore, relying on biological network alignment, we can extend the traditional "sequence-based homology" concept to the new concept of "network-based homology". Discovery of networks alignment is especially important because of its applications, such as discovering new drugs, tracking disease progression, or predicting users' behavior on social networks. In this regard, the main challenge is that the problem of finding the alignments in two graphs is NP-hard. In situations like this, we can use the relatively fast meta-heuristic algorithms to find some acceptable approximate solutions. The main contribution of this research consists of conducting a comparison over the network alignment algorithms, based on the respective evaluation criteria, execution time, memory consumption and complexity of the testing networks. The experimental results are obtained from running the relevant algorithms on the well-known BioGRID dataset. Evaluations indicate that among other methods, using genetic algorithm, memetic, particle swarm optimization, simulated annealing and the ant colony, could yield more valuable results. The named methods apply appropriate heuristics to generate and investigate only a very small subset of the whole search space with the highest probability of holding a solution; therefore, they often can find the optimal solution or some acceptable solutions in a relatively short time.
Language:
Persian
Published:
Journal of Molecular and Cellular Research, Volume:35 Issue: 1, 2022
Pages:
152 to 167
https://www.magiran.com/p2459816  
سامانه نویسندگان
  • Corresponding Author (2)
    Mohammad Ghasemzadeh
    Associate Professor Computer Engineering, University of Yazd, Yazd, Iran
    Ghasemzadeh، Mohammad
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