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Updated: Jul 29, 2025

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CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication

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    This study introduces CellEnBoost, a novel model for identifying ligand-receptor interactions (LRIs) that mediate cell-to-cell communication (CCC). CellEnBoost aids in understanding cancer development and metastasis by analyzing interactions within the tumor microenvironment.

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    Area of Science:

    • Computational biology
    • Cancer research
    • Bioinformatics

    Background:

    • Cell-to-cell communication (CCC) is crucial for multicellular organisms, influencing cancer genesis, development, and metastasis.
    • Ligand-receptor interactions (LRIs) are the primary mediators of CCC, particularly within the complex tumor microenvironment.
    • Accurate identification of LRIs is essential for understanding cancer biology and developing targeted therapies.

    Purpose of the Study:

    • To develop and validate a novel boosting-based computational model, CellEnBoost, for predicting LRIs involved in CCC.
    • To apply CellEnBoost for elucidating cell-cell communication patterns in cancer, specifically in human head and neck squamous cell carcinoma (HNSCC).
    • To provide a robust tool for inferring and visualizing complex cellular communication networks.

    Main Methods:

    • Development of CellEnBoost, an ensemble model combining Light gradient boosting machine, AdaBoost, and convolutional neural networks for LRI prediction.
    • Integration of data collection, feature extraction, and dimensional reduction techniques for robust LRI identification.
    • Application of CCC strength measurement and single-cell RNA sequencing data for LRI filtering and CCC elucidation, with results visualized via heatmap, Circos, and network plots.

    Main Results:

    • CellEnBoost demonstrated superior performance, achieving the highest Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR) across four LRI datasets.
    • Case study on HNSCC tissues revealed significant communication between fibroblasts and HNSCC cells, consistent with existing methods like iTALK.
    • The model successfully identified and visualized key LRIs, providing insights into tumor microenvironment interactions.

    Conclusions:

    • CellEnBoost is an effective and accurate computational tool for inferring cell-to-cell communication through LRI identification.
    • The findings highlight the importance of fibroblast-cancer cell communication in HNSCC, offering potential therapeutic targets.
    • This work contributes a valuable approach to cancer research, potentially aiding in diagnosis and treatment strategies.