Yunnan University, China.
School of Software, Yunnan University, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab074.
There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA-disease associations are time-saving and cost-effective that are highly desired for us.
We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA-disease association prediction (NMCMDA) for predicting multiple-category miRNA-disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA-disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA-disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA-disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.
越来越多的证据表明，miRNA 的失调通过各种潜在机制导致疾病。因此，预测 microRNA（miRNA）和疾病之间的多类别关联对于研究 miRNA 在疾病中的作用起着重要作用。此外，与耗时且昂贵的传统生物学实验相比，计算方法预测多类别 miRNA-疾病关联更节省时间和成本，这是我们非常需要的。
我们提出了一种新的基于端到端学习的神经多类别 miRNA-疾病关联预测方法（NMCMDA），用于预测多类别 miRNA-疾病关联。NMCMDA 有两个主要组成部分：（i）编码器直接在 miRNA-疾病异质网络上运行，并利用图神经网络分别学习 miRNA 和疾病的潜在表示。（ii）解码器利用学习到的潜在表示作为输入生成 miRNA-疾病关联分数。提出了多种编码器和解码器用于 NMCMDA。最后，使用关系图卷积网络编码器和神经多关系解码器（NMR-RGCN）的 NMCMDA 实现了最佳的预测性能。我们在三个实验数据集上与其他基线进行了比较。实验结果表明，在 Top-1 精度、Top-1 召回率和 Top-1 F1 方面，NMCMDA 明显优于最先进的 TDRC 方法。此外，针对两种高危人类疾病（即乳腺癌和肺癌）进行了案例研究，并且还基于 HMDD v3.2 的所有已知数据对前 10 名 miRNA-疾病类别关联进行了预测和验证，进一步验证了所提出方法的有效性和可行性。
