Lu Daying, Li Jian, Zheng Chunhou, Liu Jinxing, Zhang Qi
School of Cyber Science and Engineering, Qufu Normal University, Qufu 273165, China.
Bioengineering (Basel). 2024 Jul 4;11(7):680. doi: 10.3390/bioengineering11070680.
Accumulating scientific evidence highlights the pivotal role of miRNA-disease association research in elucidating disease pathogenesis and developing innovative diagnostics. Consequently, accurately identifying disease-associated miRNAs has emerged as a prominent research topic in bioinformatics. Advances in graph neural networks (GNNs) have catalyzed methodological breakthroughs in this field. However, existing methods are often plagued by data noise and struggle to effectively integrate local and global information, which hinders their predictive performance. To address this, we introduce HGTMDA, an innovative hypergraph learning framework that incorporates random walk with restart-based association masking and an enhanced GCN-Transformer model to infer miRNA-disease associations. HGTMDA starts by constructing multiple homogeneous similarity networks. A novel enhancement of our approach is the introduction of a restart-based random walk association masking strategy. By stochastically masking a subset of association data and integrating it with a GCN enhanced by an attention mechanism, this strategy enables better capture of key information, leading to improved information utilization and reduced impact of noisy data. Next, we build an miRNA-disease heterogeneous hypergraph and adopt an improved GCN-Transformer encoder to effectively solve the effective extraction of local and global information. Lastly, we utilize a combined Dice cross-entropy (DCE) loss function to guide the model training and optimize its performance. To evaluate the performance of HGTMDA, comprehensive comparisons were conducted with state-of-the-art methods. Additionally, in-depth case studies on lung cancer and colorectal cancer were performed. The results demonstrate HGTMDA's outstanding performance across various metrics and its exceptional effectiveness in real-world application scenarios, highlighting the advantages and value of this method.
越来越多的科学证据凸显了miRNA与疾病关联研究在阐明疾病发病机制和开发创新诊断方法方面的关键作用。因此，准确识别与疾病相关的miRNA已成为生物信息学中一个突出的研究课题。图神经网络（GNN）的进展推动了该领域的方法突破。然而，现有方法常常受到数据噪声的困扰，难以有效整合局部和全局信息，这阻碍了它们的预测性能。为了解决这个问题，我们引入了HGTMDA，这是一个创新的超图学习框架，它结合了基于重启的关联掩码随机游走和一个增强的GCN-Transformer模型来推断miRNA与疾病的关联。HGTMDA首先构建多个同构相似性网络。我们方法的一个新颖改进是引入了基于重启的随机游走关联掩码策略。通过随机掩码一部分关联数据并将其与由注意力机制增强的GCN集成，该策略能够更好地捕获关键信息，从而提高信息利用率并减少噪声数据的影响。接下来，我们构建一个miRNA-疾病异质超图，并采用改进的GCN-Transformer编码器来有效解决局部和全局信息的有效提取问题。最后，我们使用组合的Dice交叉熵（DCE）损失函数来指导模型训练并优化其性能。为了评估HGTMDA的性能，我们与最先进的方法进行了全面比较。此外，还对肺癌和结直肠癌进行了深入的案例研究。结果表明，HGTMDA在各种指标上表现出色，在实际应用场景中具有卓越的有效性，突出了该方法的优势和价值。
