College of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New District, Shanghai 201306, China.
School of Basic Medical Sciences, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Road, Pudong New District, Shanghai 201318, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae386.
The microRNAs (miRNAs) play crucial roles in several biological processes. It is essential for a deeper insight into their functions and mechanisms by detecting their subcellular localizations. The traditional methods for determining miRNAs subcellular localizations are expensive. The computational methods are alternative ways to quickly predict miRNAs subcellular localizations. Although several computational methods have been proposed in this regard, the incomplete representations of miRNAs in these methods left the room for improvement. In this study, a novel computational method for predicting miRNA subcellular localizations, named PMiSLocMF, was developed. As lots of miRNAs have multiple subcellular localizations, this method was a multi-label classifier. Several properties of miRNA, such as miRNA sequences, miRNA functional similarity, miRNA-disease, miRNA-drug, and miRNA-mRNA associations were adopted for generating informative miRNA features. To this end, powerful algorithms [node2vec and graph attention auto-encoder (GATE)] and one newly designed scheme were adopted to process above properties, producing five feature types. All features were poured into self-attention and fully connected layers to make predictions. The cross-validation results indicated the high performance of PMiSLocMF with accuracy higher than 0.83, average area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) exceeding 0.90 and 0.77, respectively. Such performance was better than all previous methods based on the same dataset. Further tests proved that using all feature types can improve the performance of PMiSLocMF, and GATE and self-attention layer can help enhance the performance. Finally, we deeply analyzed the influence of miRNA associations with diseases, drugs, and mRNAs on PMiSLocMF. The dataset and codes are available at https://github.com/Gu20201017/PMiSLocMF.
微小 RNA（miRNAs）在多个生物学过程中发挥着关键作用。通过检测其亚细胞定位，深入了解它们的功能和机制至关重要。确定 miRNAs 亚细胞定位的传统方法昂贵。计算方法是快速预测 miRNAs 亚细胞定位的替代方法。尽管在这方面已经提出了几种计算方法，但这些方法对 miRNAs 的表示不完整，为改进留下了空间。在这项研究中，开发了一种新的用于预测 miRNA 亚细胞定位的计算方法，称为 PMiSLocMF。由于许多 miRNAs 具有多个亚细胞定位，因此该方法是一个多标签分类器。采用了 miRNA 的几个特性，例如 miRNA 序列、miRNA 功能相似性、miRNA-疾病、miRNA-药物和 miRNA-mRNA 关联，以生成信息丰富的 miRNA 特征。为此，采用了强大的算法[node2vec 和图注意力自动编码器（GATE）]和一个新设计的方案来处理上述特性，生成五种特征类型。所有特征都被注入到自注意力和全连接层中进行预测。交叉验证结果表明，PMiSLocMF 的性能很高，准确率高于 0.83，平均接收者操作特征曲线下面积（AUC）和精度-召回曲线下面积（AUPR）分别超过 0.90 和 0.77，优于基于相同数据集的所有先前方法。进一步的测试证明，使用所有特征类型可以提高 PMiSLocMF 的性能，并且 GATE 和自注意力层可以帮助提高性能。最后，我们深入分析了 miRNA 与疾病、药物和 mRNAs 的关联对 PMiSLocMF 的影响。数据集和代码可在 https://github.com/Gu20201017/PMiSLocMF 上获得。
