背景:确定长链非编码RNA(lncRNA)之间的关系,microRNAs(miRNAs)和疾病的诊断非常有价值,预防,治疗和预测疾病。开发有效的计算预测方法可以降低实验成本。虽然已经提出了许多方法,他们经常治疗lncRNA-疾病关联(LDAs)的预测,miRNA-疾病关联(MDA)和lncRNA-miRNA相互作用(LMIs)作为单独的任务。能够同时预测所有三种关系的模型仍然相对稀缺。我们的目标是执行多任务预测,它不仅构建了一个统一的框架,而且还促进了lncRNAs之间信息的相互互补性,miRNA和疾病。
结果:在这项工作中,我们提出了一种新的无监督嵌入方法,称为图对比学习,用于多任务预测(GCLMTP)。我们的方法旨在预测LDA,MDAs和LMI通过同时提取lncRNAs的嵌入表示,miRNA和疾病。为了实现这一点,我们首先构建了一个三层lncRNA-miRNA-疾病异质性图(LMDHG),它根据这些实体的相似性和相关性整合了它们之间的复杂关系.接下来,我们采用基于图对比学习的无监督嵌入模型来提取lncRNAs的潜在拓扑特征,来自LMDHG的miRNA和疾病。图对比学习利用图卷积网络体系结构来最大化LMDHG的补丁表示和相应的高级摘要之间的互信息。随后,对于三个预测任务,探索了多个分类器来预测LDA,MDA和LMI得分。对两个数据集进行了全面的实验(来自数据库的旧版本和更新版本,分别)。结果显示,对于疾病相关的lncRNA和miRNA预测任务,GCLMTP优于其他现有技术方法。此外,两个数据集的案例研究进一步证明了GCLMTP准确发现新关联的能力。为了确保这项工作的可重复性,我们已经在https://github.com/sheng-n/GCLMTP公开提供了数据集和源代码。
Identifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases.
In this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA-miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP.