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    This study introduces a Dynamic Multi-scale Hypergraph Learning Framework (DMHLF) to improve the prediction of disease-related biomarkers by capturing complex RNA interactions. DMHLF enhances accuracy in identifying competitive endogenous RNA networks for biomedical research.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Network Medicine

    Background:

    • Competitive endogenous RNA (ceRNA) networks are crucial for understanding disease mechanisms.
    • Graph representation learning is vital for modeling biological networks and biomarker discovery.
    • Existing graph neural networks (GNNs) struggle with high-order interactions, long-range dependencies, and dynamic changes, limiting biomarker prediction accuracy.

    Purpose of the Study:

    • To develop an advanced graph learning framework, DMHLF, for accurate prediction of disease-associated ceRNA biomarkers.
    • To overcome limitations of traditional GNNs in capturing complex, multi-scale, and dynamic molecular interactions.
    • To enhance the identification of reliable ceRNA biomarkers for disease research.

    Main Methods:

    • Constructing disease-specific ceRNA regulatory networks by integrating multiple RNA types (miRNAs, lncRNAs, circRNAs, mRNAs) and diseases.
    • Employing a Hypergraph-Weighted Dynamic Random Walk (HEDRW) for dynamic meta-embedding extraction of high-order regulatory information.
    • Utilizing a residual-enhanced hypergraph neural network with spectral analysis and a cross-scale attention mechanism for feature fusion and high-quality node embeddings.

    Main Results:

    • DMHLF significantly outperforms existing methods in predicting disease-associated ceRNA biomarkers across diverse datasets.
    • Experimental validation confirms the framework's ability to capture both local and global regulatory patterns.
    • The proposed methods effectively address issues like topological information loss and oversmoothing inherent in traditional GNNs.

    Conclusions:

    • DMHLF provides a robust and accurate framework for predicting disease-related ceRNA biomarkers.
    • The study highlights the importance of multi-scale and dynamic graph learning for complex biological networks.
    • DMHLF serves as a valuable predictive tool for advancing biomedical research and personalized medicine.