一种基于分子图形特征和残余块的深度学习方法,用于预测CircRNA和RBP之间的相互作用位点
1Jiangxi Science and Technology Normal University, No. 589 Xuefu Avenue, Hongjiao Zhou, Nanchang City, Jiangxi Province, China.
Biochemical and biophysical research communications
|September 25, 2025
概括
MGFCRSites是一个新的深度学习工具,可以预测圆形RNA (circRNAs) 上的RNA结合蛋白位. 它有效地模拟circRNA分子结构,推进基因调节和疾病研究的研究.
科学领域:
- 计算生物学 计算生物学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 循环RNAs (circRNAs) 在生物过程和疾病中至关重要.
- RNA结合蛋白 (RBP) 相互作用是基因调节和RNA修饰的关键.
- 对于RNA-RBP结合位点的现有计算方法主要集中在线性RNA上,忽略了circRNAs.
研究的目的:
- 开发一种新的深度学习框架,MGFCRSites,用于预测circRNAs上的RBP结合位点.
- 为了解决RBP-circRNA相互作用的尚未探索的领域.
- 为分析RBP-circRNA相互作用提供一个强大的计算工具.
主要方法:
- 将circRNA分子结构编码为ASCII表示.
- 使用图形卷积网络原理构建circRNAs的分子图.
- 使用剩余块来进行层次特征提取,以及用于绑定站点识别的预测模块.
主要成果:
- 在37个基准数据集中,MGFCRSites实现了高性能 (AUC:0.9663).
- 该框架有效地捕捉了circRNAs的化学结构模式.
- MGFCRSites在预测circRNAs上的RBP结合位点方面表现优于现有的方法.
结论:
- MGFCRSites是第一个明确模拟circRNA化学分子结构的计算方法,用于结合部位预测.
- 该工具为分析RBP-circRNA相互作用提供了一个强大的解决方案.
- 这项研究促进了对基因调节机制和疾病研究的理解.
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