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Identification of Essential Proteins using a Novel Multi-objective Optimization Method

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posted on 06.02.2020 by Chong Wu, Houwang Zhang, Le Zhang, Hanying Zheng

Using graph theory to identify essential proteins is a hot topic at present. These methods are called network-based methods. However, the generalization ability of most network-based methods is not satisfactory. Hence, in this paper, we consider the identification of essential proteins as a multi-objective optimization problem and use a novel multi-objective optimization method to solve it. The optimization result is a set of Pareto solutions. Every solution in this set is a vector which has a certain number of essential protein candidates and is considered as an independent predictor or voter. We use a voting strategy to assemble the results of these predictors. To validate our method, we apply it on the protein-protein interactions (PPI) datasets of two species (Yeast and Escherichia coli). The experiment results show that our method outperforms state-of-the-art methods in terms of sensitive, specificity, F-measure, accuracy, and generalization ability.

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Email Address of Submitting Author

chongwu2-c@my.cityu.edu.hk

ORCID of Submitting Author

0000-0003-3405-742X

Submitting Author's Institution

City University of Hong Kong

Submitting Author's Country

China

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