Jiang Jipu, Yan Cheng
School of Informatics, Hunan University of Chinese Medicine, Changsha, China.
IET Syst Biol. 2025 Jan-Dec;19(1):e70023. doi: 10.1049/syb2.70023.
Many studies have shown that microRNAs (miRNAs) play key roles in some important processes and human complicated diseases. In addition, they also have specific physiological roles at different cellular sites. Therefore, identifying their subcellular localisation is very urgent to systemically understand their physiological functions. In this study, we propose a computational method, called PMLocMSCAM, to predict miRNA subcellular localisation based on miRNA similarities and cross-attention mechanism. PMLocMSCAM implements a multimodal integration framework that systematically processes miRNA sequence data, miRNA-mRNA association networks with mRNA subcellular localisation annotations, miRNA-disease associations, and miRNA-drug association networks. The architecture initiates with intrinsic feature extraction through Smith-Waterman alignment for sequence similarity computation and disease ontology-based functional similarity derivation. Subsequent heterogeneous network embedding employs Node2vec for topological feature learning across three interaction modalities (miRNA-disease, miRNA-drug, and miRNA-mRNA networks), enhanced by hypergraph convolution to capture higher-order relationships through incidence matrix decomposition. Localisation-specific patterns are propagated via miRNA-mRNA interaction weights, culminating in a multi-head attention mechanism that dynamically fuses five feature matrices-miRNA sequence features, miRNA-disease association features, miRNA-drug association features, miRNA-mRNA association features, and miRNA-mRNA localisation features. These integrated representations are processed through residual-connected multilayer perceptrons to generate probabilistic predictions across seven subcellular compartments, establishing an end-to-end computational paradigm for multimodal miRNA localisation analysis. In order to assess the prediction performance of our method and compare it with other miRNA subcellular localisation computational methods, we conduct 10-fold cross validation (10-CV) and independent test dataset. The AUC (area of receiver operating characteristic curve) and AUPR (area of precision-recall curve) are used as metrics. The experiment results show that the average AUC and AUPR values exceed 0.9182 and 0.8487 on 10-CV, respectively. The AUC and AUPR values also reach 0.9157 and 0.8469 on independent test dataset, respectively. It is superior with compared methods. The ablation experiment results also further that PMLocMSCAM can effective predict miRNA subcellular localisations and provide help to understand their physiological functions.
许多研究表明，微小RNA（miRNA）在一些重要过程和人类复杂疾病中发挥着关键作用。此外，它们在不同的细胞位点也具有特定的生理作用。因此，确定它们的亚细胞定位对于系统地了解其生理功能非常迫切。在本研究中，我们提出了一种名为PMLocMSCAM的计算方法，用于基于miRNA相似性和交叉注意力机制预测miRNA亚细胞定位。PMLocMSCAM实现了一个多模态整合框架，系统地处理miRNA序列数据、带有mRNA亚细胞定位注释的miRNA-mRNA关联网络、miRNA-疾病关联以及miRNA-药物关联网络。该架构首先通过Smith-Waterman比对进行内在特征提取，用于序列相似性计算和基于疾病本体的功能相似性推导。随后的异构网络嵌入采用Node2vec进行跨三种相互作用模式（miRNA-疾病、miRNA-药物和miRNA-mRNA网络）的拓扑特征学习，并通过超图卷积增强，以通过关联矩阵分解捕获高阶关系。特定定位模式通过miRNA-mRNA相互作用权重进行传播，最终形成多头注意力机制，动态融合五个特征矩阵——miRNA序列特征、miRNA-疾病关联特征、miRNA-药物关联特征、miRNA-mRNA关联特征和miRNA-mRNA定位特征。这些整合的表示通过残差连接的多层感知器进行处理，以生成跨越七个亚细胞区室的概率预测，建立了一个用于多模态miRNA定位分析的端到端计算范式。为了评估我们方法的预测性能并将其与其他miRNA亚细胞定位计算方法进行比较，我们进行了10折交叉验证（10-CV）和独立测试数据集。使用AUC（受试者工作特征曲线面积）和AUPR（精确召回曲线面积）作为指标。实验结果表明，在10折交叉验证中，平均AUC和AUPR值分别超过0.9182和0.8487。在独立测试数据集上，AUC和AUPR值也分别达到0.9157和0.8469。它优于比较方法。消融实验结果也进一步表明，PMLocMSCAM可以有效地预测miRNA亚细胞定位，并为理解其生理功能提供帮助。
