Identify potential circRNA-disease associations through a multi-objective evolutionary algorithm

Abstract

More and more studies have demonstrated that circRNAs can be used as markers of various diseases due to their inherent stability. Numerous computational methods, especially artificial intelligence approaches, have been applied to the prediction of circRNA-disease associations. However, the objective functions of these prediction methods are usually single and standard. The single objective function hardly reflects the characteristics of the prediction problem, and leads to low prediction accuracy. There has never been a method to design a set of objective functions for the circRNA-disease prediction problem and solve it by using intelligent optimization algorithms. In this paper, a method ICDMOE is proposed to identify circRNA-disease associations via a multi-objective evolutionary algorithm. A solution space is defined and four objective functions are designed based on matrix factorization and modularity of similarity networks. An improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) is also employed to solve these objective functions. In the MOEA/D, an adaptive penalty-based boundary intersection strategy is proposed, which not only ensures convergence, but also guarantees the diversity of solutions in the Pareto front. Finally, the experimental results show that ICDMOE has better performance than pure matrix factorization-based methods, non-adaptive MOEA/D-based methods and other prediction methods. Furthermore, we find that the predicted disease-circRNAs can be confirmed by existing studies, miRNA regulations and expression profiles, etc. These indicate that ICDMOE can provide good candidates for biomedical experiments.