使用多图对比学习预测非编码RNA和疾病关联
Si-Lin Sun1,2, Yue-Yi Jiang1,2, Jun-Ping Yang1,2
1College of Information Science Technology, Hainan Normal University, Haikou, 571158, China.
Scientific reports
|January 2, 2025
概括
这项研究介绍了K-MGCMLD,这是一种新的深度学习方法,用于预测非编码RNA (miRNA, lncRNA) 和疾病之间的关联. 它在识别这些关键的生物关系方面取得了很高的准确性,从而改善了诊断.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 医学中的人工智能
背景情况:
- 非编码RNAs,包括微RNAs (miRNAs) 和长非编码RNAs (lncRNAs),在生物过程中发挥关键作用.
- 准确预测非编码RNA与疾病的关联对于早期疾病诊断和了解疾病机制至关重要.
- 现有的深度学习方法往往具有较低的预测准确性,并且仅限于单个RNA类型疾病关联.
研究的目的:
- 开发一种先进的深度学习模型,K-Means和多图对比学习,用于预测miRNA,lncRNA和疾病之间的关联 (K-MGCMLD).
- 通过提高预测准确度,克服现有方法的局限性,并使多个非编码RNA与疾病相关性的同时预测成为可能.
主要方法:
- 构建了一个整合miRNAs,lncRNAs和疾病的异质图.
- 雇佣的K-意味着对下方样本进行集群,以平衡正负样本.
- 利用图形卷积网络 (GCN) 编码器和多图形对比学习来提取特征并捕获拓特征.
- 应用了XGBoost分类器,用于使用重建的特征进行多关联分类预测.
主要成果:
- 达到高的曲线下面面积 (AUC) 值:miRNA疾病为0.9542,lncRNA疾病为0.9603,lncRNA-miRNA关联为0.9687.
- 案例分析验证了肺癌和阿尔茨海默氏症前30个预测的miRNA关联,证明了其实用性.
结论:
- K-MGCMLD有效地预测了高准确度的多个非编码RNA疾病关联.
- 提出的方法为疾病关联预测的计算方法提供了显著的进步.
- 经过验证的预测突出了K-MGCMLD在临床应用中识别疾病相关的非编码RNA方面的潜力.
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