Cross-patient cell-type annotation in single-cell RNA sequencing (scRNA-seq) remains challenging due to pronounced interpatient heterogeneity and distribution shifts across patient-specific cellular contexts. Conventional annotation approaches often rely on proximity-driven graph construction or expression similarity, which may introduce spurious cell-cell connections and lead to unstable knowledge transfer across patients. To address this limitation, we propose PathoGraph, a functionally guided graph learning framework for robust cross-patient cell-type annotation. The proposed method integrates KEGG-7-based biosemantic graph structure learning with cross-patient representation adaptation. Specifically, pathway-derived functional semantic profiles are incorporated to refine patient-specific cell graphs, encouraging biologically coherent neighborhoods and suppressing noise introduced by purely expression-based similarity. Based on the refined graphs, a cross-patient representation adaptation mechanism further aligns embeddings between labeled reference patients and unlabeled query patients to facilitate reliable annotation transfer. Experiments on three cross-patient scRNA-seq data sets, including leukemia, breast invasive carcinoma, and colorectal cancer data sets, demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks. Across all tasks, PathoGraph obtained an average ACC of 84.28% and an F1-score of 84.08%, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods. Ablation studies further show that removing the biosemantic graph learning module reduces the average accuracy to 83.48%, highlighting the importance of functional-guided graph refinement. In addition, post hoc functional relevance analyses in immune-cell and cancer-associated contexts suggest that the learned cell-cell graphs capture biologically relevant neighborhood structures beyond expression-driven proximity. The source code and processed data are publicly available at: https://github.com/LiYuechao1998/PathoGraph.
Yue C. Li, Mengmeng Wei, Xinfei Wang et al.· Journal of Chemical Informat...· 0 citations
Microbe-drug association (MDA) prediction is of great importance for understanding drug action mechanisms and exploring microbiome-based therapeutic strategies. However, when confronted with extremely sparse biological networks with pronounced structural heterogeneity, existing methods often struggle to simultaneously model global topological dependencies and local structural disparities. To address these challenges, we propose MuSeL, a multi-scale adaptive graph representation learning framework that jointly mitigates the above issues from two complementary perspectives: global topology modeling and local structure optimization. Specifically, we develop a spectral kernel attention mechanism that leverages the normalized Laplacian and chebyshev polynomial expansion to efficiently capture multi-scale global topological relations in the spectral domain. Meanwhile, we construct a structure-aware self-adaptive sampling module that dynamically adjusts neighborhood sampling based on node clustering coefficients and degree centrality, thereby improving the reliability of local structural and feature representations. Finally, the fused multi-scale node embeddings are fed into a prediction module to estimate MDA scores. Extensive experiments show that outperforms existing mainstream models, while ablation studies and case analyses further validate its effectiveness and robustness. Overall, MuSeL provides a practical computational framework for prioritizing candidate MDAs, supporting downstream biological validation and drug repurposing research.
Yuehu Wu, Lei Wang, Zhengwei Li et al.· Proceedings of the 32nd ACM...· 0 citations
MicroRNAs (miRNAs) are critical regulators in biological processes such as cell proliferation, differentiation, and apoptosis, with their aberrant expression strongly linked to a range of complex diseases. Because traditional experimental methods for predicting miRNA-disease associations (MDAs) are both time-intensive and costly, computational models offer an efficient alternative. Graph neural networks (GNNs) have shown promise in MDAs prediction. However, existing models often suffer from limitations, including inadequate neighborhood information aggregation, inflexible propagation schemes, and an imbalance between global and local information. To address these issues, this paper presents a novel GNN framework, APKAGN, designed for predicting miRNA-disease associations. APKAGN enhances performance through three innovative mechanisms: 1) Adaptive local propagation, leveraging a gated recursion module to dynamically adjust propagation depth while employing residual connections to preserve multi-scale features. 2) Multi-subspace global aggregation, capturing global topology information via multi-dimensional projection and density-aware KNN selection. 3) Dynamic feature fusion, integrating local and global representations using an attention-based gating mechanism. Evaluated on the updated HMDD v3.2 dataset across multiple independent random seeds, APKAGN achieved an outstanding average AUC of 95.09%, an accuracy of 88.23%, and an F1-score of 88.34%, outperforming seven state-of-the-art baseline models. Case studies on lymphoma, prostate, and breast tumors further demonstrated the predictive performance of the proposed model, with 26, 25, and 27 of the top 30 predicted miRNAs validated in the dbDEMC and miR2Disease databases, respectively. By leveraging adaptive propagation and dynamic KNN mechanisms, APKAGN significantly enhances the accuracy of MDAs prediction, offering a powerful tool for investigating disease mechanisms and identifying biomarkers.
Ru Nie, Yingkai Li, Zhengwei Li et al.· IEEE transactions on computa...· 0 citations