Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.

中文翻译：

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GCRFLDA：使用带有条件随机场的图卷积矩阵完成对 lncRNA 疾病关联进行评分

长链非编码RNA（lncRNA）在各种生物调控过程中发挥着重要作用，与疾病的发生发展密切相关。识别lncRNA-疾病关联对于揭示疾病的分子机制和探索治疗策略具有重要意义。因此，有必要通过计算预测 lncRNA 与疾病的关联作为生物学实验的补充方法。在本研究中，我们提出了一种基于图卷积矩阵补全的新型预测方法 GCRFLDA。GCRFLDA 首先使用可用的 lncRNA 疾病关联信息构建了一个图表。然后，它构建了一个由条件随机场和注意力机制组成的编码器来学习节点的有效嵌入，以及一个解码器层来对 lncRNA 疾病关联进行评分。在 GCRFLDA 中，将高斯交互谱核相似性和余弦相似性融合为 lncRNA 和疾病节点的辅助信息。四个基准数据集的实验结果表明，GCRFLDA 优于其他现有方法。此外，我们对四种疾病进行了案例研究，观察到 80 种预测的相关 lncRNA 中有 70 种被文献证实。