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Author

Mengmeng Wei

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Book Open access Aug 2026

Multi-Hop Diffusion-Wavelet Semantic Learning for Zero-Shot Circrna-Mirna Interaction Prediction

Circular RNAs (circRNAs) and microRNAs (miRNAs) are key regulators in various biological processes, and identifying their interactions is vital for elucidating disease mechanisms. However, due to the high experimental cost and limited coverage of known circRNA-miRNA interactions (CMIs), there is a pressing need for com...

Mengmeng Wei, Lei Wang, Peng-Wei Hu et al. · 0 citations
Book Open access Aug 2026

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

MuSeL is proposed, 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.

Yuehu Wu, Lei Wang, Zhengwei Li et al. · 0 citations
Open access Aug 2026

NARVGA: A Hybrid Framework Integrating Matrix Factorisation and Adversarial Graph Learning for circRNA-Disease Association Prediction

A computer-based method that combines several types of biological information to predict likely relationships between circular ribonucleic acids and diseases is developed and shows a strong ability to distinguish known associations from unconfirmed ones.

Mianshuo Lu, Mengmeng Wei, Changchun Liu et al. · 0 citations
Jul 2026

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

Experiments on three cross-patient scRNA-seq data sets demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods.

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

MuSeL is proposed, 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.

Yuehu Wu, Lei Wang, Zhengwei Li et al. · 0 citations

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