电子学报2025，Vol.53Issue(9)：3103-3116,14.DOI:10.12263/DZXB.20250436
面向环状RNA-疾病关联预测的物态分析优化算法
Optimization Algorithm for State Analysis of CircRNA-Disease Association Prediction
摘要
Abstract
Extensive studies have shown that circular RNA(circRiboNucleic Acid),as a type of endogenous non-cod-ing RNA,plays a key role in the occurrence and development of various complex human diseases.Through mechanisms such as acting as molecular sponges,regulating gene transcription,or interacting with proteins,circRNAs participate in the regulation of disease-related signaling pathways.Analyzing the associations between circRNAs and diseases is of crucial scientific value for deepening the understanding of disease mechanisms,discovering novel biomarkers,and advancing preci-sion medicine.However,traditional experimental methods are constrained by high costs,long cycles,and limited through-put,which severely restrict large-scale analysis of circRNA-disease associations.Thus,developing efficient and low-cost computational methods is essential for promoting research in this field.In response,this paper proposes a prediction model named ES-NMGCDA based on evolutionary computation.The model first constructs multi-source similarity networks of circRNAs and diseases,then incorporates the state analysis optimization algorithm(SAOA)to integrate and optimize these multi-source similarity networks,and finally employs a causal forest classifier to achieve accurate prediction of circRNA-disease associations.By integrating the powerful search advantage of SAOA with the superior inference capability of causal forests,ES-NMGCDA enables highly accurate and robust prediction of potential circRNA-disease associations.To compre-hensively evaluate the performance of the ES-NMGCDA model,we conducted rigorous 5-fold cross-validation on the wide-ly used public benchmark dataset CircR2Disease.Experimental results demonstrate that the model achieved a prediction ac-curacy of 93.80%,while also excelling in multiple metrics such as precision and sensitivity,significantly outperforming sev-eral existing baseline methods.Furthermore,to validate the model's practical utility in real biomedical scenarios,we carried out two case studies.In the case study on circRNA-disease associations,18 out of the top 20 circRNA-disease pairs with the highest prediction scores were supported by recent literature.In the case study focused on breast cancer,43 out of the top 50 predicted circRNAs were confirmed to be closely associated with the disease.These results consistently indicate that the ES-NMGCDA model not only provides highly reliable candidate circRNA molecules for subsequent molecular biology experi-ments,significantly shortening research cycles and reducing experimental costs,but also offers new data support and theo-retical foundations for understanding the role of circRNAs in complex diseases.关键词
多源相似性网络/环状RNA-疾病/演化计算/物态分析优化算法/因果森林/潜在关联Key words
multi-source similarity network/circRNA-disease/evolutionary computation/state analysis optimiza-tion algorithm/causal forest/potential association分类
信息技术与安全科学引用本文复制引用
王政,王磊,尤著宏,赵博伟..面向环状RNA-疾病关联预测的物态分析优化算法[J].电子学报,2025,53(9):3103-3116,14.基金项目
国家自然科学基金(No.62172355,No.62176146,No.62325308) National Natural Science Foundation of China(No.62172355,No.62176146),No.62325308) （No.62172355,No.62176146,No.62325308）
