Fu Laiyi, Zhou Yangyi, Lyu Hongqiang, Sun Hequan
School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, Shannxi, China.
Research Institute of Xi'an Jiaotong University, Zhejiang, Hangzhou 311200, Zhejiang, China.
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf389.
Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play pivotal roles in various human diseases. Predicting associations such as lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), and lncRNA-miRNA interactions (LMIs) is crucial for understanding disease mechanisms and identifying therapeutic targets. However, existing models face significant challenges in handling extreme data imbalance and often treat multiple ncRNA-disease and ncRNA-ncRNA interactions collectively, lacking the ability to provide precise, differentiated predictions for specific types of ncRNAs. This limitation reduces their practical applicability. To address these issues, we propose the Dual Balanced Augmented Topological Noncoding RNA Disease triplet Association (DBATNDA) model. DBATNDA constructs an Interaction Dual Graph with LDAs, MDAs, and LMIs as nodes and introduces an efficient graph-based balanced topological augmentation mechanism to enhance node structural representation and adaptability to imbalanced data. This innovative approach enables fast and accurate predictions of ncRNA-disease and ncRNA-ncRNA triplet associations through node classification view. To the best of our knowledge, no existing method employs such a dual-representation strategy to provide simultaneously differentiated predictions for the associations between diverse ncRNAs and diseases while also enhancing target specificity. Experimental results demonstrate DBATNDA's superior performance compared to state-of-the-art models, while case studies confirm its practical significance in these triple association prediction. The code and datasets are publicly available at https://github.com/AI4Bread/DBATNDA.
非编码RNA（ncRNAs），包括长链非编码RNA（lncRNAs）和微小RNA（miRNAs），在各种人类疾病中发挥着关键作用。预测诸如lncRNA-疾病关联（LDA）、miRNA-疾病关联（MDA）和lncRNA-miRNA相互作用（LMI）等关联对于理解疾病机制和识别治疗靶点至关重要。然而，现有模型在处理极端数据不平衡方面面临重大挑战，并且通常将多个ncRNA-疾病和ncRNA-ncRNA相互作用一起处理，缺乏为特定类型的ncRNAs提供精确、差异化预测的能力。这种局限性降低了它们的实际适用性。为了解决这些问题，我们提出了双平衡增强拓扑非编码RNA疾病三联体关联（DBATNDA）模型。DBATNDA构建了一个以LDA、MDA和LMI为节点的相互作用双图，并引入了一种基于图的高效平衡拓扑增强机制，以增强节点结构表示和对不平衡数据的适应性。这种创新方法能够通过节点分类视图快速准确地预测ncRNA-疾病和ncRNA-ncRNA三联体关联。据我们所知，现有的方法都没有采用这种双表示策略来同时为不同的ncRNAs与疾病之间的关联提供差异化预测，同时还能提高目标特异性。实验结果表明，与现有最先进的模型相比，DBATNDA具有卓越的性能，而案例研究证实了其在这些三联体关联预测中的实际意义。代码和数据集可在https://github.com/AI4Bread/DBATNDA上公开获取。
