MVGNCDA:识别基于多视图卷积网络和网络嵌入的潜在circRNA疾病关联
Guicong Sun1, Mengxin Zheng1, Yongxian Fan2
1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Interdisciplinary sciences, computational life sciences
|February 17, 2025
概括
本研究介绍了MVGNCDA,这是一个用于识别循环RNA疾病关联的计算框架. 它有效地预测使用多视图卷积网络和网络嵌入的未知链接,克服数据限制.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 循环RNAs (circRNAs) 越来越多地被认为是它们在疾病发展中的作用.
- 识别circRNA疾病关联的传统方法耗时且昂贵.
- 现有的计算方法与数据稀疏性和不高效的相似性表示作斗争.
研究的目的:
- 开发一个创新的计算框架,MVGNCDA,用于预测circRNA-疾病关联.
- 利用多源数据,更有效地检测未知关联.
- 克服现有的计算方法在circRNA疾病关联预测的局限性.
主要方法:
- 计算疾病的语义相似性,circRNA的功能相似性,高斯相互作用概况 (GIP) 内核,和等号相似性.
- 使用多视图卷积网络 (GCN) 来提取本地节点嵌入.
- 构建一个具有整合相似性和经过验证的关联的异质网络,以学习全球节点嵌入.
- 使用双线解码器来评估基于融合的本地和全球嵌入的circRNA疾病关联.
主要成果:
- 与现有方法相比,MVGNCDA在五个公共数据集中表现出卓越的性能.
- 五次交叉验证证实了拟议框架的有效性.
- 一个案例研究验证了MVGNCDA在有效识别未知的circRNA疾病关联方面的能力.
结论:
- MVGNCDA提供了一种有效的计算解决方案,用于预测circRNA与疾病的关联.
- 该框架成功地集成了多源数据和先进的网络嵌入技术.
- MVGNCDA通过有效发现新型circRNA与疾病的联系来推动该领域的发展.
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