A Meta-Heuristic Approach to Predict Protein-Protein Interaction Network

Chowdhury, A. and Rakshit, P. and Konar, A. and Nagar, Atulya K. (2016) A Meta-Heuristic Approach to Predict Protein-Protein Interaction Network. In: IEEE Congress on Evolutionary Computation 2016, 24-29 July 2016, Vancouver, Canada.

7. CEC-16882-A Meta-Heuristic Approach to Predict Protein-Protein Interaction Network.pdf

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Protein interactions are central to structural and
functional organization of the cell. Understanding biological processes thus relies on a comprehensive knowledge of different types of protein–protein interactions (PPIs) and interaction mechanisms. This paper formulates the PPI prediction problem as a multi-objective optimization 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 multi-objective optimization problem is solved using a fusion of the differential evolution for multi-objective optimization and the stochastic learning automata. Here the former is employed to globally explore the search space and the latter for the adaptive tuning of the control parameters of the algorithm. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods with respect to sensitivity, specificity, and F1 score.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Keywords: protein–protein interaction networks; annotation; accessible solvent area; stochastic learning automata; differential evolution for multi-objective optimization.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 02 Jun 2016 14:35
Last Modified: 24 Nov 2016 11:44
URI: https://hira.hope.ac.uk/id/eprint/1438

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