多尺度邻居拓引导变压器和科尔莫戈罗夫-阿诺德网络增强功能学习模型用于与疾病相关的circRNA预测
IEEE journal of biomedical and health informatics
|August 20, 2025
概括
这项研究引入了MKCD,这是一种通过整合多尺度邻近拓和高级特征学习来预测循环RNA (circRNA) 和疾病关联的新方法. MKCD显著提高了预测准确度,有助于了解疾病机制.
科学领域:
- 生物信息学
- 计算生物学
- 基因组学
背景情况:
- 循环非编码RNAs (circRNAs) 与人类疾病有关,使其准确的关联预测对理解疾病发病至关重要.
- 目前的预测方法通常基于图形学习,难以完全捕捉多尺度邻近拓和属性依赖.
研究的目的:
- 开发一个先进的计算模型来预测circRNA疾病的关联.
- 通过识别关键circRNA来增强对疾病机制的理解.
主要方法:
- 提出了MKCD,这是一个集成多尺度邻近拓和Kolmogorov-Arnold网络 (KAN) 增强特征学习的模型.
- 通过异质图的随机走路使用自适应的多尺度邻居嵌入 (AMNE).
- 使用动态多尺度邻居拓引导变压器 (DMTT) 进行关系学习.
- 整合了一个特征关网 (FGN) 用于特征重要性评估.
- 应用适应性联合卷积神经网络和KAN学习策略 (ACK) 进行依赖学习.
主要成果:
- MKCD的表现优于六种最先进的方法,AUC和AUPR的改善分别为至少14. 1%和7. 6%.
- 废除研究证实了单个成分 (AMNE,DMTT,FGN,ACK) 的有效性.
- 案例研究成功地确定了三种疾病的可靠circRNA候选物.
结论:
- MKCD提供了一个强大的和有效的方法来预测circRNA疾病的关联.
- 该模型能够整合多层次的拓信息和高级特征学习提供了显著的优势.
- 在疾病相关的circRNA发现和机制研究方面,MKCD具有前景.
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