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Identification of Essential Proteins using a Novel Multi-objective Optimization Method
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  • Chong Wu ,
  • Houwang Zhang ,
  • Le Zhang ,
  • Hanying Zheng
Chong Wu
City University of Hong Kong

Corresponding Author:[email protected]

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Houwang Zhang
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Hanying Zheng
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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.