Protein-protein interaction network prediction using stochastic learning automata induced differential evolution

Display Omitted Formulation of PPI problem as a single objective optimization problem.Two novel approaches are proposed to predict a PPI network by independently maximizing individual objectives.Individual objectives are optimized by differential evolution (DE) and stochastic learning automata (SLA).DE is employed to globally explore the search space and SLA for adaptive tuning of the control parameters of the algorithm.The proposed technique outperforms the existing PPI prediction methods. Protein-protein interactions (PPIs) are of biological interest for their active participation in coordinating a number of cellular processes in living organisms. This paper attempts to formulate PPIs as an optimization problem with an aim to independently maximize (a) the stability of a complex formed by two proteins predicted to be interacting, (b) the difference between their individual accessible solvent area and that of the corresponding protein-protein complex, (c) their functional similarity and d) the occurrence of their interacting domain pairs. The novelty of the paper lies in ranking the set of PPI networks, obtained through independently optimizing individual objectives, using two approaches. The first approach is concerned with identifying the equally good PPI networks based on their fitness-based ranks with respect to individual four objectives. The second approach aims at sorting the PPI networks based on their fuzzy memberships to satisfy individual four objectives. The paper also proposes a novel single objective optimization algorithm to optimize individual objectives, influencing the true prediction of a PPI network. The proposed algorithm is realized by an amalgamation of the differential evolution and the stochastic learning automata, where the former is employed to globally explore the search space and the latter for the adaptive tuning of the control parameter of the algorithm. The proposed technique outperforms the existing methods, including relative specific similarity, domain cohesion coupling, random decision forest, fuzzy support vector machine and evolutionary/swarm algorithm based approaches, with respect to both sensitivity and specificity.

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