格亨:通过图形嵌入和异质图形注意力网络来预测circRNA疾病关联
1School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
Computational biology and chemistry
|May 5, 2024
概括
这项研究介绍了GEHGAN,这是一种用于预测循环RNA (circRNA) -疾病关联的计算方法. GEHGAN利用图形嵌入和注意网络来提高预测准确性,为传统实验提供了具有成本效益的替代方案.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 循环RNAs (circRNAs) 越来越多地被认为是它们在人类疾病中的作用.
- 湿实验以确定circRNA与疾病的关系是昂贵且耗时的.
- 现有的计算方法与不平衡的节点分布作斗争,限制了它们的性能.
研究的目的:
- 开发一种新的计算方法,GEHGAN,用于预测circRNA与疾病的关联.
- 解决平衡节点分布现有方法的局限性,以提高准确性.
主要方法:
- 使用circRNA序列的相似性,circRNA-RNA结合蛋白 (RBP) 相互作用和疾病语义信息构建了一个异质图.
- 应用了一种带有随机走路 (跳跃和停留策略) 的图形嵌入技术,用于初始circRNA和疾病嵌入.
- 利用多头图注意力网络和多层感知器 (MLP) 来改进嵌入和预测新型circRNA疾病关系.
主要成果:
- 在CircR2Diseasev2.0数据库中,GEHGAN实现了高性能,AUC得分为0.9829和AUPR值为0.9815 .
- 关于骨髓瘤,胃和结直肠瘤的案例研究表明,该模型在确定相关的circRNA疾病相关性方面的有效性.
结论:
- GEHGAN提供了一种强大而高效的计算方法,用于预测circRNA与疾病的关联.
- 该方法能够处理异质图形结构并改进节点表示的能力提高了其预测能力.
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