School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China.
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac159.
In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA-disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA-disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging.
To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA-disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA-disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA.
https://github.com/chengxu123/MINIMDA and http://120.79.173.96/.
近年来，大量的生物学实验强烈表明 miRNAs 在理解疾病发病机制方面发挥着重要作用。miRNA-疾病关联的发现有利于疾病的诊断和治疗。由于通过生物实验推断这些关联既耗时又昂贵，因此研究人员寻求利用计算方法来识别这些关联。图卷积网络（GCN）在链接预测问题上表现出色，已成功应用于 miRNA-疾病关联预测。然而，GCN 仅考虑一层的一阶邻域信息，但无法捕获来自高阶邻居的信息，无法通过信息传播学习 miRNA 和疾病的表示。因此，如何以显式的方式有效地聚合来自高阶邻域的信息仍然具有挑战性。
为了解决这一挑战，我们提出了一种名为混合邻域信息用于 miRNA-疾病关联预测（MINIMDA）的新方法，该方法可以融合多模态网络中 miRNA 和疾病的混合高阶邻域信息。首先，MINIMDA 分别使用多源信息构建集成 miRNA 相似性网络和集成疾病相似性网络。然后，通过融合来自多模态网络的混合高阶邻域信息，即集成 miRNA 相似性网络、集成疾病相似性网络和 miRNA-疾病关联网络，获得 miRNA 和疾病的嵌入表示。最后，我们集中 miRNA 和疾病的多模态嵌入表示，并将它们输入多层感知机（MLP）以预测它们潜在的关联。大量实验结果表明，MINIMDA 在总体上优于其他最先进的方法。此外，对食管癌、结肠癌和肺癌的案例研究进一步证明了 MINIMDA 的有效性。
https://github.com/chengxu123/MINIMDA 和 http://120.79.173.96/。
