Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa067.
As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various human diseases, identifying disease-related lncRNAs is important for understanding the pathogenesis of complex diseases. Most of current data-driven methods for disease-related lncRNA candidate prediction are based on diseases and lncRNAs. Those methods, however, fail to consider the deeply embedded node attributes of lncRNA-disease pairs, which contain multiple relations and representations across lncRNAs, diseases and miRNAs. Moreover, the low-dimensional feature distribution at the pairwise level has not been taken into account. We propose a prediction model, VADLP, to extract, encode and adaptively integrate multi-level representations. Firstly, a triple-layer heterogeneous graph is constructed with weighted inter-layer and intra-layer edges to integrate the similarities and correlations among lncRNAs, diseases and miRNAs. We then define three representations including node attributes, pairwise topology and feature distribution. Node attributes are derived from the graph by an embedding strategy to represent the lncRNA-disease associations, which are inferred via their common lncRNAs, diseases and miRNAs. Pairwise topology is formulated by random walk algorithm and encoded by a convolutional autoencoder to represent the hidden topological structural relations between a pair of lncRNA and disease. The new feature distribution is modeled by a variance autoencoder to reveal the underlying lncRNA-disease relationship. Finally, an attentional representation-level integration module is constructed to adaptively fuse the three representations for lncRNA-disease association prediction. The proposed model is tested over a public dataset with a comprehensive list of evaluations. Our model outperforms six state-of-the-art lncRNA-disease prediction models with statistical significance. The ablation study showed the important contributions of three representations. In particular, the improved recall rates under different top $k$ values demonstrate that our model is powerful in discovering true disease-related lncRNAs in the top-ranked candidates. Case studies of three cancers further proved the capacity of our model to discover potential disease-related lncRNAs.
由于长非编码 RNA（lncRNA）的异常与各种人类疾病密切相关，因此鉴定与疾病相关的 lncRNA 对于理解复杂疾病的发病机制很重要。目前大多数基于数据驱动的疾病相关 lncRNA 候选预测方法都是基于疾病和 lncRNA。然而，这些方法未能考虑 lncRNA-疾病对中深深嵌入的节点属性，这些属性包含 lncRNA、疾病和 miRNA 之间的多种关系和表示。此外，还没有考虑到对点点级别的低维特征分布。我们提出了一种预测模型 VADLP，以提取、编码和自适应地整合多层次的表示。首先，构建了一个三层异质图，带有加权的层间和层内边，以整合 lncRNA、疾病和 miRNA 之间的相似性和相关性。然后，我们定义了三个表示，包括节点属性、对点点拓扑和特征分布。节点属性是通过嵌入策略从图中提取出来的，用于表示 lncRNA-疾病的关联，这些关联是通过它们的共同 lncRNA、疾病和 miRNA 推断出来的。对点点拓扑是通过随机游走算法构建的，并通过卷积自动编码器进行编码，以表示 lncRNA-疾病对之间隐藏的拓扑结构关系。新的特征分布是通过方差自动编码器建模的，以揭示 lncRNA-疾病关系的潜在规律。最后，构建了一个注意表示级别的集成模块，以自适应地融合三个表示，用于 lncRNA-疾病关联预测。该模型在一个包含完整评估列表的公共数据集上进行了测试。与六种最先进的 lncRNA-疾病预测模型相比，我们的模型具有统计学意义上的优势。消融研究表明了三种表示的重要贡献。特别是，在不同的 top $k$ 值下，我们的模型提高了召回率，这表明我们的模型在发现排名靠前的候选物中的真实疾病相关 lncRNA 方面具有强大的能力。三种癌症的案例研究进一步证明了我们的模型发现潜在疾病相关 lncRNA 的能力。
