A Tri-Gene Ontology Migration Operator for Improving the Performance of Meta-heuristics in Complex Detection Problems

Authors

  • Isra H. Abdulateef Department of Computer Science, College of Science, Al-Mustansiriyah University, Baghdad, Iraq
  • Dhia A. Jumaa Alzubaydi Al-Rasheed University College, Baghdad, Iraq
  • Bara'a Ali Attea Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-2790-8724

DOI:

https://doi.org/10.24996/ijs.2023.64.3.33

Keywords:

Evolutionary algorithm, functional similarity, gene sub-ontology, protein-protein interaction network, semantic similarity

Abstract

      Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulated under four expressions depending on the type of gene sub-ontology. To demonstrate the performance of the proposed evolutionary based complex detection algorithm, the Saccharomyces Cerevisiae (yeast) PPI network is used in the evaluation. The results reveal that the proposed algorithm achieves more accurate complex structures than the counterpart heuristic algorithms and the canonical evolutionary algorithm based on the topological-aware mutation operator.

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Published

2023-03-30

Issue

Section

Computer Science

How to Cite

A Tri-Gene Ontology Migration Operator for Improving the Performance of Meta-heuristics in Complex Detection Problems. (2023). Iraqi Journal of Science, 64(3), 1426-1441. https://doi.org/10.24996/ijs.2023.64.3.33

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