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Mengmeng Wei

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Jul 2026

Functionally Guided Graph Learning for Robust Cross-Patient Cell-Type Annotation in Single-Cell RNA Sequencing.

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. · 0 citations
Book Open access Aug 2026

MuSeL: A Multi-Scale Adaptive Graph Representation Learning Framework for Microbe-Drug Association Prediction

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. · 0 citations