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    A new AI framework, PPF-HGNN, unifies chemical-disease-gene prediction tasks. It achieves state-of-the-art results, accelerating drug discovery by deciphering complex biological relationships.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Artificial Intelligence in Drug Discovery

    Background:

    • Chemical-Disease-Gene (CDG) association prediction is crucial for drug discovery, including target identification and drug repurposing.
    • Existing methods often isolate Chemical-Disease (CD), Disease-Gene (DG), and Chemical-Gene (CG) interactions, missing synergistic relationships.
    • There's a need for unified frameworks to capture shared semantics and cross-task dependencies in CDG prediction.

    Purpose of the Study:

    • To develop a unified pretraining framework for CDG association prediction that learns transferable biomedical semantics.
    • To enable efficient adaptive fine-tuning for CD, DG, and CG tasks using prompt tuning without full retraining.
    • To introduce the Pretraining-Prompt-Finetuning Heterogeneous Graph Neural Network (PPF-HGNN) model.

    Main Methods:

    • Constructed a CDG heterogeneous graph incorporating CD, DG, and CG interactions.
    • Employed parameter-free metapath-guided message passing for capturing high-order semantic information.
    • Utilized a dual self-supervised objective (association prediction + feature reconstruction) for pretraining generalizable representations.
    • Implemented task-specific prompt tuning with learnable prompt vectors for adaptive fine-tuning via additive fusion.

    Main Results:

    • PPF-HGNN achieved state-of-the-art performance across CD, CG, and DG association prediction tasks.
    • Achieved AUC scores of 0.9633 (CD), 0.9939 (CG), and 0.9390 (DG).
    • Achieved F1-scores of 0.9157 (CD), 0.9668 (CG), and 0.8955 (DG), outperforming six baseline methods.

    Conclusions:

    • The pretrain-prompt-finetune paradigm is effective for multi-task biomedical association prediction.
    • PPF-HGNN provides a robust AI-driven tool for accelerating translational research.
    • The model successfully deciphers complex chemical-disease-gene relationships, enhancing drug discovery efforts.