Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

中文翻译：

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基于多层图嵌入聚合预测LncRNA-疾病关联的Extra Trees方法

许多实验研究揭示了 lncRNA 与疾病之间的重要关联。识别准确的关联将为疾病治疗提供新的视角。已经开发了基于计算的方法来解决这些问题，但是这些方法有一些局限性。在本文中，我们提出了一种名为 MLGCNET 的准确方法来发现潜在的 lncRNA 疾病关联。首先，我们利用前k个相似信息为lncRNA和疾病重建了相似性网络，并构建了一个lncRNA-疾病异构网络（LDN）。然后，我们在 LDN 上应用多层图卷积网络来获得节点的潜在特征表示。最后，Extra Trees 用于计算疾病和 lncRNA 之间关联的概率。广泛的 5 折交叉验证实验的结果表明，与最先进的方法相比，MLGCNET 具有更优越的预测性能。案例研究证实了我们的模型对特定疾病的表现。所有实验结果都证明了 MLGCNET 在预测潜在的 lncRNA 疾病关联方面的有效性和实用性。