A Multi-Objective Evolutionary Approach to Evaluate the Designing Perspective of Protein-Protein Interaction Network

Authors

  • Archana Chowdhury Department of Electronics and Tele-Communication Engineering
  • Amit Konar Department of Electronics and Tele-Communication Engineering
  • Pratyusha Rakshit Department of Electronics and Tele-Communication Engineering
  • Atulya K. Nagar Department of Mathematics and Computer Science, Liverpool Hope University

Keywords:

protein-protein interaction; phylogenetic profile; CHARMM energy; non dominated sorting bee colony optimization;clustering coefficient

Abstract

Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes. Since protein interactions determine the outcome of most cellular processes, so identifying and characterizing Protein– Protein interactions and their networks are essential for understanding the mechanisms of biological processes on a molecular level. This paper explores the application of Nondominated Sorting Bee Colony (NSBC) optimization algorithm to the Protein- Protein Interaction (PPI) identification problem. In this work, PPI is formulated as a multi-objective optimization problem. The proposed scheme determines an optimal solution based on the binding energy, mismatch in phylogenetic profiles of two bound proteins and clustering coefficients. Results are demonstrated for three different networks both numerically and pictorially. Experimental results reveal that the proposed method outperforms Differential Evolution for Multi-objective Optimization (DEMO), Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Artificial Bee Colony (ABC), and Differential Evolution (DE).

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Published

2013-10-01

How to Cite

Archana Chowdhury, Amit Konar, Pratyusha Rakshit, & Atulya K. Nagar. (2013). A Multi-Objective Evolutionary Approach to Evaluate the Designing Perspective of Protein-Protein Interaction Network . Journal of Network and Innovative Computing, 1, 20. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/36

Issue

Vol. 1 (2013)

Section

Original Article

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