Zhiyi Lin

Drug repositioning (DR) presents a cost-effective strategy for drug development by identifying novel therapeutic applications for existing drugs. Current computational approaches remain constrained by their inability to synergize localized substructure patterns with global network semantics, leading to overreliance on data augmentation to mitigate latent drug–disease association (DDA) information gaps. To address these limitations, we present multi-view stacked graph convolutional network (MVSGDR), a novel DR framework featuring three technical innovations: (i) multi-view stacked module that enables depth-wise feature enhancement through hierarchical aggregation of multi-hop neighborhood interactions across distinct graph convolutional layers; (ii) bi-level subgraph transformer module that decomposes DDAs into METIS (a graph partitioning tool) informative subgraphs for breadth-wise analysis of external and internal subgraph drug–disease relationships; and (iii) negative sampling balancing strategy that mitigates sample imbalance through negative sample synthesis. Extensive 10-fold cross-validation experiments across four benchmark datasets confirm MVSGDR’s superior performance, demonstrating its statistically significant improvements over existing methods. Moreover, case studies further validate MVSGDR’s potential utility through identification of previously unreported DDAs with supporting literature evidence.

Discovering miRNAs associated with diseases can contribute to understanding the pathogenesis and treatment strategies of diseases. In the commonly used graph regularized non-negative matrix factorization methods for miRNA-disease association prediction, there exist issues such as interference from low-dimensional matrix noise and loss of network topology information from partial original data. To solve these issues, we propose a method called L $_{21}$ S-NPFM, which combines L $_{21}$ similarity constrain graph matrix factorization and network projection fusion for miRNA-disease association prediction. Firstly, we introduce a similarity constraint term based on the L $_{21}$ -norm (L $_{21}$ SGMF) into matrix factorization, effectively suppressing noise in the low-dimensional matrix. Secondly, we design a network projection fusion method (NPFM) to integrate the consistency projection matrices of miRNA/disease networks and initial score matrices, compensating for the lost network topology information. Experimental results from LOOCV and 5-fold CV findings show that L $_{21}$ S-NPFM works better than six other mainstream methods. Additionally, case studies show its accuracies of up to 100% for 10 miRNAs associated with diabetic nephropathy (DN) and 80% for 10 miRNAs associated with thoracic aortic aneurysm (TAA), respectively.

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases.

为了提升经配准高分辨率遥感影像对变化检测的精度，基于ChangeFormer提出了一种将移动卷积与相对注意力相结合的孪生网络(mobile convolution and relative attention Siamese network, MCRASN)。该网络以垂直布局结合移动卷积和相对注意力，构建多阶段组合编码器替换原网络编码器，高效地捕捉所需的多尺度细节特征和像素间相互关系信息，改进差异模块为1个可学习的距离度量模块进行距离计算，同时通过引入EFL（equalized focal loss）损失函数解决数据集正负样本失衡的问题以实现精确的变化检测。实验结果表明，所提出的MCRASN算法在LEVIR-CD数据集上具有更好的变化检测性能，其精确率、召回率、F1得分和总体精度分别为93.94%、89.26%、91.54%和99.18%，优于先前的多种检测方法。