Prediction of protein-protein interaction network using a multi-objective optimization approach

J Bioinform Comput Biol. 2016 Jun;14(3):1650008. doi: 10.1142/S0219720016500086. Epub 2015 Dec 14.

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.

Keywords: Protein–protein interaction networks; firefly algorithm; gene ontology; nondominated sorting.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Gene Ontology
  • Protein Domains
  • Protein Interaction Mapping / methods*
  • Saccharomyces cerevisiae Proteins / metabolism

Substances

  • Saccharomyces cerevisiae Proteins