植物 lncRNA-miRNA 相互作用预测基于反事实异构图注意力网络
Yu He1, ZiLan Ning1, XingHui Zhu1
1College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China.
Interdisciplinary sciences, computational life sciences
|October 9, 2024
概括
这项研究介绍了CFHAN,一种新的图形神经网络方法,用于预测植物中长时间的非编码RNA-microRNA相互作用. CFHAN提高了预测准确性和对噪声的稳定性,为植物研究提供了宝贵的见解.
科学领域:
- 计算生物学 计算生物学
- 植物分子生物学 植物分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 长非编码RNAs (lncRNAs) 和microRNAs (miRNAs) 在调节植物生命过程中起着至关重要的作用.
- 对于理解这些调节网络,lncRNAs和miRNAs (LMIs) 之间的相互作用至关重要.
- 计算方法,特别是图形神经网络 (GNN),越来越多地用于预测LMI,但现有的方法受到低语义图形信息和噪声的影响.
研究的目的:
- 开发一种强大而准确的计算方法,用于预测植物lncRNA-miRNA相互作用 (LMIs).
- 解决现有的基于GNN的方法的局限性,特别是它们对噪声和低语义图形数据的敏感性.
- 增强对植物分子生物学中的监管关系的理解.
主要方法:
- 构建基于现实世界的 lncRNA-miRNA (L-M) 异质网络.
- 开发一种新的反事实异构图注意网络 (CFHAN),结合节点级注意,语义级注意和反事实链接.
- 使用增强的节点嵌入作为多层感知器 (MLP) 的输入来预测LMI.
主要成果:
- 与基准工厂LMI数据集中的五种最先进的方法相比,CFHAN表现优越.
- 实现了高预测准确度,平均AUC为0.9953和平均ACC为0.9733.
- 展示了有前途的跨物种预测能力.
结论:
- CFHAN有效地预测了工厂LMI,其稳定性和准确性得到了改进.
- 该方法为实验性植物LMI研究提供了宝贵的见解.
- CFHAN代表了对植物监管相互作用的计算预测的重大进展.
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