基于核心组合和图形卷积网络的LncRNA-蛋白相互作用的预测
IEEE journal of biomedical and health informatics
|June 16, 2023
概括
预测长非编码RNA-蛋白相互作用 (LPI) 对于理解生物调节至关重要. 一个新的框架,LPI-KCGCN,使用内核方法和图形卷积网络来准确预测这些重要的LPIs.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 长非编码RNAs (lncRNAs) 和蛋白质形成了调节各种生物过程的复合体.
- 对lncRNA-蛋白相互作用 (LPI) 的实验验证是资源密集的.
- 对LPIs的计算预测提供了一个可扩展的替代方案.
研究的目的:
- 开发一个高效的计算框架来预测 lncRNA-蛋白相互作用 (LPI).
- 为了利用序列,表达式和功能特征来提高LPI预测准确度.
主要方法:
- 从lncRNA和蛋白质特征 (序列,相似性,表达,基因本体学) 构建内核矩阵.
- 重建内核矩阵和集成已知的LPIs以形成图形拓.
- 采用双层图形卷积网络 (GCN) 来进行表示学习.
- 组合多个LPI-KCGCN变体进行最终预测.
主要成果:
- 在平衡和不平衡数据集上,LPI-KCGCN框架实现了高性能.
- 在一个含有15.5%阳性样本的数据集上,AUC达到0.9714和AUPR达到0.9216.
- 在高度不平衡的数据集 (5%的阳性样本) 上,AUC为0.9907,AUPR为0.9267,表现优于现有方法.
结论:
- LPI-KCGCN提供了一种强大而准确的计算方法,用于预测lncRNA-蛋白相互作用.
- 该框架表现出卓越的性能,特别是在不平衡的生物数据上.
- 这种方法可以加快功能性lncRNA-蛋白质复合物的发现.
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