An Evolutionary Algorithm for Improving the Quantity and Quality of the Detected Complexes from Protein Interaction Networks

Authors

DOI:

https://doi.org/10.24996/ijs.2024.65.5.42

Keywords:

EA, Gene ontology, protein complex, protein interaction networks, modularity density

Abstract

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed gene ontology-based mutation operator. The performance of the proposed EA to have a high quantity and quality of the detected complexes is assessed on two yeast PPINs and compared with two benchmarking gold complex sets. The reported results reveal the ability of modularity density to be more productive in detecting more complexes with high quality when teamed up with a gene ontology-based mutation operator.

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Author Biography

  • Bara'a Ali Attea, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

     

     

     

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Published

2024-05-30

Issue

Section

Computer Science

How to Cite

An Evolutionary Algorithm for Improving the Quantity and Quality of the Detected Complexes from Protein Interaction Networks. (2024). Iraqi Journal of Science, 65(5), 2898-2924. https://doi.org/10.24996/ijs.2024.65.5.42

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