Abstract
This paper formulates the protein-protein interaction (PPI) prediction problem as a multi-objective optimization (MOO) problem. The focus here is to jointly maximize i) the number of common neighbors of the proteins predicted to be interacting, ii) their functional similarity, and iii) the ratio between their individual accessible solvent area and that of the corresponding protein-protein complex. The above MOO problem is solved using a fusion of the differential evolution for multi-objective optimization and the stochastic learning automata. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods with respect to sensitivity, specificity, and F1 score.
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