An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection

Authors

  • Isra H. Abdulateef Department of Computer Science, College of Science, Al-Mustansiriyah University, Baghdad, Iraq / Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq
  • Dhia A. Alzubaydi Department of Computer Science, College of Science, Al-Mustansiriyah University, Baghdad, Iraq / 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.2023.64.4.34

Keywords:

Evolutionary algorithm, gene ontology, protein complex, protein-protein interaction network, semantic similarity

Abstract

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA with more biological consistency. For this purpose, a new crossover operator is suggested where biological information in terms of both gene semantic similarity and protein functional similarity is fed into its design. To reflect the heuristic roles of both semantic and functional similarities, this paper introduces two gene ontology (GO) aware crossover operators. These are direct annotation-aware and inherited annotation-aware crossover operators. The first strategy is handled with the direct gene ontology annotation of the proteins, while the second strategy is handled with the directed acyclic graph (DAG) of each gene ontology term in the gene product. To conduct our experiments, the proposed EAs with GO-aware crossover operators are compared against the state-of-the-art heuristic, canonical EAs with the traditional crossover operator, and GO-based EAs. Simulation results are evaluated in terms of recall, precision, and F measure at both complex level and protein level. The results prove that the new EA design encourages a more reliable treatment of exploration and exploitation and, thus, improves the detection ability for more accurate protein complex structures.

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Published

2023-04-30

Issue

Section

Computer Science

How to Cite

An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection. (2023). Iraqi Journal of Science, 64(4), 1975-1987. https://doi.org/10.24996/ijs.2023.64.4.34

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