Li Zhengwei, Bai Xu, Nie Ru, Liu Yanyan, Zhang Lei, You Zhuhong
IEEE J Biomed Health Inform. 2024 Dec;28(12):7611-7622. doi: 10.1109/JBHI.2024.3438439. Epub 2024 Dec 5.
Extensive research indicates that microRNAs (miRNAs) play a crucial role in the analysis of complex human diseases. Recently, numerous methods utilizing graph neural networks have been developed to investigate the complex relationships between miRNAs and diseases. However, these methods often face challenges in terms of overall effectiveness and are sensitive to node positioning. To address these issues, the researchers introduce DARSFormer, an advanced deep learning model that integrates dynamic attention mechanisms with a spectral graph Transformer effectively. In the DARSFormer model, a miRNA-disease heterogeneous network is constructed initially. This network undergoes spectral decomposition into eigenvalues and eigenvectors, with the eigenvalue scalars being mapped into a vector space subsequently. An orthogonal graph neural network is employed to refine the parameter matrix. The enhanced features are then input into a graph Transformer, which utilizes a dynamic attention mechanism to amalgamate features by aggregating the enhanced neighbor features of miRNA and disease nodes. A projection layer is subsequently utilized to derive the association scores between miRNAs and diseases. The performance of DARSFormer in predicting miRNA-disease associations (MDAs) is exemplary. It achieves an AUC of 94.18% in a five-fold cross-validation on the HMDD v2.0 database. Similarly, on HMDD v3.2, it records an AUC of 95.27%. Case studies involving colorectal, esophageal, and prostate tumors confirm 27, 28, and 26 of the top 30 associated miRNAs against the dbDEMC and miR2Disease databases, respectively.
广泛的研究表明，微小RNA（miRNA）在复杂人类疾病的分析中起着至关重要的作用。最近，已经开发了许多利用图神经网络的方法来研究miRNA与疾病之间的复杂关系。然而，这些方法在整体有效性方面常常面临挑战，并且对节点定位敏感。为了解决这些问题，研究人员引入了DARSFormer，这是一种先进的深度学习模型，它有效地将动态注意力机制与谱图Transformer集成在一起。在DARSFormer模型中，首先构建一个miRNA-疾病异质网络。该网络经过谱分解为特征值和特征向量，随后将特征值标量映射到向量空间。采用正交图神经网络来优化参数矩阵。然后将增强后的特征输入到图Transformer中，该图Transformer利用动态注意力机制通过聚合miRNA和疾病节点的增强邻居特征来融合特征。随后利用一个投影层来推导miRNA与疾病之间的关联分数。DARSFormer在预测miRNA-疾病关联（MDA）方面的性能堪称典范。在HMDD v2.0数据库的五折交叉验证中，它的AUC达到了94.18%。同样，在HMDD v3.2上，它的AUC记录为95.27%。涉及结直肠癌、食管癌和前列腺癌肿瘤的案例研究分别针对dbDEMC和miR2Disease数据库确认了前30个相关miRNA中的27个、28个和26个。
