A Node Importance Analysis Method for Solving Drug Target Identification Problems

Drug target identification is a constraint multi-objective optimization problem with NP-hard characteristics, which aims to select a set of nodes from a gene network to control the transition between disease and normal state. However, existing related studies mainly focus on the evolutionary strategies, ignoring the relationship between nodes and constraint optimization. Therefore, this paper proposes a novel node importance method by analyzing the relationship between adjacent nodes with different connectivity differences. Then, the node importance is utilized to design a population initialization strategy to select better nodes. In experiments, this strategy is embedded into three algorithms to verify its effectiveness. Experimental results on three cancer genomic datasets show that the algorithm incorporating node importance knowledge achieves better performance compared to the original algorithm regarding two multiobjective optimization indicators and one biological significance indicator.