Wu Qing-Wen, Cao Rui-Fen, Xia Jun-Feng, Ni Jian-Cheng, Zheng Chun-Hou, Su Yan-Sen
IEEE/ACM Trans Comput Biol Bioinform. 2022 Nov-Dec;19(6):3171-3178. doi: 10.1109/TCBB.2021.3113122. Epub 2022 Dec 8.
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.
大量实验研究揭示了长链非编码RNA（lncRNAs）与疾病之间的显著关联。识别准确的关联将为疾病治疗提供新的视角。基于计算的方法已被开发用于解决这些问题，但这些方法存在一些局限性。在本文中，我们提出了一种名为MLGCNET的准确方法，以发现潜在的lncRNA-疾病关联。首先，我们使用前k个相似信息为lncRNAs和疾病重建相似性网络，并构建了一个lncRNA-疾病异质网络（LDN）。然后，我们在LDN上应用多层图卷积网络以获得节点的潜在特征表示。最后，使用Extra Trees来计算疾病与lncRNA之间的关联概率。广泛的5折交叉验证实验结果表明，与现有方法相比，MLGCNET具有卓越的预测性能。案例研究证实了我们的模型在特定疾病上的性能。所有实验结果证明了MLGCNET在预测潜在lncRNA-疾病关联方面的有效性和实用性。
