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    This study introduces MAN-SDNE, a computational framework for predicting molecular interactions in human cells. It systematically analyzes associations between long non-coding RNAs, microRNAs, proteins, drugs, and diseases for better disease understanding.

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    Area of Science:

    • Biomedical research
    • Computational biology
    • Systems biology

    Background:

    • Post-genomic research aims to understand complex molecular interactions in human cells.
    • Existing studies often focus on limited pairwise molecular associations.
    • A systematic approach is needed to analyze multifaceted biomolecular networks.

    Purpose of the Study:

    • To develop a computational framework, MAN-SDNE, for predicting diverse intermolecular associations.
    • To construct a comprehensive human molecular association network.
    • To provide a tool for systematic exploration of molecular interactions and their link to diseases.

    Main Methods:

    • Constructed a large-scale human molecular association network integrating long non-coding RNA, microRNA, protein, drug, and disease interactions.
    • Utilized network representation learning (MAN-SDNE) to capture node features based on network proximity and attributes.
    • Employed a Random Forest classifier trained on these features to predict intermolecular associations.

    Main Results:

    • MAN-SDNE achieved high predictive performance with an AUC of 0.9552 and AUPR of 0.9338.
    • Demonstrated the framework's capability through a case study on long non-coding RNA-protein interactions.
    • The network contains 6,528 molecular nodes and 105,546 associations across 9 types.

    Conclusions:

    • MAN-SDNE offers a systematic insight into synergistic molecular associations and complex diseases.
    • The developed framework provides a valuable network-based computational tool for exploring intermolecular interactions.
    • This approach enhances the understanding of molecular mechanisms underlying human health and disease.