title: Prediction of protein-protein interaction network using a multi-objective optimization approach.
abstract: Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.
authors: 1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India.
authors: Archana
authors: A
authors: Chowdhury
authors: 1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India.
authors: Pratyusha
authors: P
authors: Rakshit
authors: 1 Artificial Intelligence Laboratory, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India.
authors: Amit
authors: A
authors: Konar
mesh_terms: D000465:Algorithms; D063990:Gene Ontology; D000072417:Protein Domains; D025941:Protein Interaction Mapping / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins
keywords: Protein–protein interaction networks; firefly algorithm; gene ontology; nondominated sorting