面向环状RNA-疾病关联预测的物态分析优化算法

王政; 王磊; 尤著宏; 王磊; 赵博伟

doi:10.12263/DZXB.20250436

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面向环状RNA-疾病关联预测的物态分析优化算法

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- 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
- 电子学报 2025年53卷第9期页码：3103-3116
- 作者机构：
  
  1.西安理工大学计算机科学与工程学院，陕西西安 710048
  2.西北工业大学计算机学院，陕西西安 710072
  3.中国矿业大学计算机科学与技术学院，江苏徐州 221116
  4.浙江大学药学院，浙江杭州 310058
- 作者简介：
  
  [ "王政男，1991年1月出生于陕西省临潼市.现为西安理工大学计算机科学与工程学院博士研究生.主要研究方向为演化计算、数据挖掘和机器学习.E-mail: xywangzheng0971@163.com" ]
  [ "王磊男，1972年5月出生于陕西省白水市.现为西安理工大学计算机科学与工程学院教授、博士生导师.主要研究方向为进化算法、神经网络与数据挖掘.中国电子学会会员编号：E190002758S. E-mail: leiwang@xaut.edu.cn" ]
  [ "尤著宏男，1980年8月出生于甘肃省兰州市.现为西北工业大学计算机学院教授，博士生导师.主要研究方向为大数据分析、数据挖掘及其在生物信息学上的应用等.中国电子学会会员编号：E190188767M. zhuhongyou@nwpu.edu.cn" ]
  [ "王磊男，1982年1月出生于山东省枣庄市. 现为中国矿业大学计算机科学与技术学院、人工智能学院教授，博士生导师. 获国家自然科学基金、省自然科学基金等项目7项. 在国内外发表学术论文150余篇. 中国电子学会会员编号：E190002758S.E-mail: leiwang@cumt.edu.cn" ]
  [ "赵博伟男，1996年12月出生于陕西省宝鸡市.现为浙江大学药学院特别资助博士后、助理研究员.主要研究方向为机器学习、知识图谱及其在生物信息学中的应用.E-mail: zhaobowei@zju.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62172355;62176146;62325308);No.62325308
- DOI：10.12263/DZXB.20250436
  中图分类号： TP399
- 收稿：2025-05-29，
  
  录用：2025-09-24，
  
  纸质出版：2025-09-25
- 稿件说明：
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王政, 王磊, 尤著宏, 等. 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(09): 3103-3116.

WANG Zheng, WANG Lei, YOU Zhu-hong, et al. Optimization Algorithm for State Analysis of CircRNA-Disease Association Prediction[J]. Acta Electronica Sinica, 2025, 53(09): 3103-3116.
王政, 王磊, 尤著宏, 等. 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(09): 3103-3116. DOI：10.12263/DZXB.20250436

WANG Zheng, WANG Lei, YOU Zhu-hong, et al. Optimization Algorithm for State Analysis of CircRNA-Disease Association Prediction[J]. Acta Electronica Sinica, 2025, 53(09): 3103-3116. DOI：10.12263/DZXB.20250436

摘要

大量研究表明，环状RNA（circRiboNucleic Acid）作为一种内源性非编码RNA，在多种人类复杂疾病的发生和发展中扮演着关键角色.它们通过充当分子海绵、调节基因转录或与蛋白质相互作用等多种机制参与疾病相关信号通路的调控.解析环状RNA与疾病间的关联关系，对于深入理解疾病发生机制、发现新型生物标志物以及推动精准医疗的发展具有至关重要的科学价值.然而，传统实验方法成本高、周期长、通量有限，严重制约了环状RNA与疾病间关联关系的大规模解析.因此，发展高效、低成本的计算方法，对推动环状RNA与疾病关联的解析研究至关重要.本文据此提出了一种基于演化计算的预测模型ES-NMGCDA.该模型首先构建了多种环状RNA与疾病的多源相似性网络，随后加入物态分析优化算法（State Analysis Optimization Algorithm，SAOA）对多源相似性网络进行融合与优化，最终利用因果森林分类器实现环状RNA-疾病关联关系的精准预测.ES-NMGCDA通过将物态分析优化算法的强大搜索优势与因果森林的卓越推理能力相结合，实现了对环状RNA与疾病间潜在关联的高精度、高稳健性预测.为全面评估ES-NMGCDA模型的性能，我们在广泛使用的公共基准数据集CircR2Disease上进行了严格的5折交叉验证.实验结果表明，本模型在测试中达到了93.80%的预测准确率，同时在精确率、敏感率等多项指标上均表现优异，显著优于多种现有基线方法.此外，为进一步验证模型在真实生物医学场景下的实用价值，我们还开展了两项案例研究：在环状RNA与疾病间关联性的案例研究中，模型预测得分最高的前20个环状RNA-疾病关联对中，有18个获得了最新文献的支持；而在针对乳腺癌的案例研究中，模型预测出的前50个环状RNA中有43个已被证实与乳腺癌密切相关.这些结果一致表明，ES-NMGCDA模型不仅能够为后续分子生物学实验提供高可信度的候选环状RNA分子清单，显著缩短研究周期并降低实验成本，也为深入理解环状RNA在复杂疾病中的作用机制提供了新的数据支持和理论依据.

Abstract

Extensive studies have shown that circular RNA (circRiboNucleic Acid)

as a type of endogenous non-coding 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 precision medicine. However

traditional experimental methods are constrained by high costs

long cycles

and limited throughput

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 comprehensively evaluate the performance of the ES-NMGCDA model

we conducted rigorous 5-fold cross-validation on the widely used public benchmark dataset CircR2Disease. Experimental results demonstrate that the model achieved a prediction accuracy of 93.80%

while also excelling in multiple metrics such as precision and sensitivity

significantly outperforming several 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 experiments

significantly shortening research cycles and reducing experimental costs

but also offers new data support and theoretical foundations for understanding the role of circRNAs in complex diseases.

关键词

Keywords

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