广泛的研究表明 microRNA (miRNA) 在复杂人类疾病的分析中发挥着至关重要的作用。最近，人们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，这些方法往往在整体有效性方面面临挑战，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，它将动态注意力机制与谱图 Transformer 有效地集成在一起。在DARSFormer模型中，首先构建了miRNA-疾病异质网络。该网络经历谱分解为特征值和特征向量，特征值标量随后被映射到向量空间。采用正交图神经网络来细化参数矩阵。然后将增强的特征输入到图 Transformer 中，该 Transformer 利用动态注意机制通过聚合 miRNA 和疾病节点的增强邻居特征来合并特征。随后利用投影层得出 miRNA 与疾病之间的关联评分。 DARSFormer 在预测 miRNA 与疾病关联方面的表现堪称典范。在 HMDD v2.0 数据库上的五倍交叉验证中，其 AUC 达到 94.18%。同样，在 HMDD v3.2 上，其 AUC 为 95.27%。涉及结直肠癌、食管癌和前列腺癌的案例研究分别证实了 dbDEMC 和 miR2Disease 数据库中前 30 个相关 miRNA 中的 27 个、28 个和 26 个。 DARSFormer 的代码和数据可在 https://github.com/baibaibaialone/DARSFormer 访问。

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 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. The code and data for DARSFormer are accessible at https://github.com/baibaibaialone/DARSFormer.