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