School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, Anhui, China.
BMC Bioinformatics. 2022 Jul 11;23(1):271. doi: 10.1186/s12859-022-04819-3.
MircoRNAs (miRNAs) play a central role in diverse biological processes of Camellia sinensis var.assamica (CSA) through their associations with target mRNAs, including CSA growth, development and stress response. However, although the experiment methods of CSA miRNA-target identifications are costly and time-consuming, few computational methods have been developed to tackle the CSA miRNA-target association prediction problem.
In this paper, we constructed a heterogeneous network for CSA miRNA and targets by integrating rich biological information, including a miRNA similarity network, a target similarity network, and a miRNA-target association network. We then proposed a deep learning framework of graph convolution networks with layer attention mechanism, named MTAGCN. In particular, MTAGCN uses the attention mechanism to combine embeddings of multiple graph convolution layers, employing the integrated embedding to score the unobserved CSA miRNA-target associations.
Comprehensive experiment results on two tasks (balanced task and unbalanced task) demonstrated that our proposed model achieved better performance than the classic machine learning and existing graph convolution network-based methods. The analysis of these results could offer valuable information for understanding complex CSA miRNA-target association mechanisms and would make a contribution to precision plant breeding.
微小 RNA（miRNAs）通过与靶 mRNAs 的相互作用，在茶树（CSA）的各种生物过程中发挥核心作用，包括 CSA 的生长、发育和应激反应。然而，尽管 CSA miRNA 靶标鉴定的实验方法成本高、耗时多，但针对 CSA miRNA-靶标关联预测问题的计算方法却很少。
在本文中，我们通过整合丰富的生物学信息，包括 miRNA 相似性网络、靶标相似性网络和 miRNA-靶标关联网络，构建了一个 CSA miRNA 和靶标的异构网络。然后，我们提出了一个基于图卷积网络的深度学习框架，称为 MTAGCN。特别是，MTAGCN 使用注意力机制来结合多个图卷积层的嵌入，并使用集成嵌入来对未观察到的 CSA miRNA-靶标关联进行评分。
在两个任务（平衡任务和不平衡任务）上的综合实验结果表明，我们提出的模型比经典的机器学习和现有的基于图卷积网络的方法具有更好的性能。这些结果的分析可以为理解复杂的 CSA miRNA-靶标关联机制提供有价值的信息，并为精准植物育种做出贡献。
