ICDNSGA: Identification of Potential circRNA-Disease Associations Based on Improved Non-Dominated Sorting Genetic
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Increasing biological research indicates that the expression levels of circRNAs fluctuate during the onset of various diseases, making them potential biomarkers for multiple conditions. Although numerous artificial intelligence-based computational methods are currently employed for circRNA-disease associations prediction, these methods often rely on a single objective function, which can lead to suboptimal prediction accuracy. To date, no method has designed a set of multi-objective functions specifically for the circRNA-disease prediction problem and optimized it using a non-dominated sorting genetic algorithm. This paper introduces a novel approach by utilizing multi-objective functions and an improved non-dominated sorting genetic algorithm (ICDNSGA) to identify potential associations of circRNA-disease. The method constructs a solution space through matrix factorization and network community characteristics, designing four distinct objective functions optimized via the enhanced multi-objective non-dominated sorting genetic algorithm. ICDNSGA incorporates a population-based adaptive normalization strategy, improving algorithm convergence and solution diversity. Experimental results show that ICDNSGA outperforms pure matrix factorization methods, non-dominated sorting genetic algorithms and other machine learning techniques in predictive performance. Additionally, the prediction results can be validated through existing research and biological analyses, underscoring ICDNSGA's potential as a valuable tool for biomedical experimentation.
