­­­An Enhanced Genetic Algorithm for Determining the Pathways in Protein-Protein Interaction Networks

Abstract

Abstract Biological pathway plays a significant role in understanding evolution and cell activities of any organism. For finding the pathways in PPI networks, it is important to orient Protein-Protein Interaction (PPI) that will be in the forms of undirected networks. It indicates that orienting protein interactions can enhance the pathway discovery process. To overcome the drawbacks in the existing algorithms, an Enhanced Genetic Algorithm (EGA) has been proposed to reduce the unnecessary edges and discover the pathways in PPI networks. The experimental results of the proposed and the existing algorithms such as Genetic Algorithm (GA), Random Orientation Algorithm plus Local Search (ROLS), Maximum Constraint Satisfaction (MAX-CSP), Minimum Satisfiability (MIN-SAT) were compared. The experiments are carried out using BioGRID databases and it is inferred that the proposed enhanced genetic algorithm has achieved better results in addressing this problem compared to other existing techniques. Also, it is inferred that the proposed EGA technique performs better in terms of execution, fitness function and specifically in matching gold standard pathways.