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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Related Experiment Video

Updated: Feb 5, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Beyond Curated Knowledge: Structural Protein Embeddings Enhance GNN-Based Personalized Cancer Prognosis.

Sofia Ormazabal Arriagada, Tsung-Wei Lin, Marta Misztal

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    |February 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    We developed GLLM, a novel graph learning framework integrating gene expression, clinical data, and protein structures to predict 5-year cancer risk. This approach enhances prognostic accuracy for personalized patient surveillance and treatment prioritization.

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    Area of Science:

    • Oncology
    • Bioinformatics
    • Machine Learning

    Background:

    • Accurate cancer prognostic models are crucial for treatment planning and resource allocation.
    • Current models often lack integration of multi-omics data and structural protein information.
    • Stratifying patients by risk is essential for personalized follow-up schedules.

    Purpose of the Study:

    • To introduce GLLM, a multimodal graph learning framework for cancer patient risk stratification.
    • To integrate RNA-seq profiles, clinical variables, and protein structural embeddings for improved prognostic accuracy.
    • To evaluate GLLM's performance across multiple cancer types compared to existing methods.

    Main Methods:

    • GLLM utilizes a graph neural network with genes as nodes within a protein-protein interaction graph.
    • A novel fusion mechanism, SCANE, integrates patient-specific gene expression with structural protein embeddings.
    • The framework processes RNA-seq data, clinical variables, and protein structural information.

    Main Results:

    • GLLM demonstrated improved area under the precision-recall curve across breast, lung, and colorectal cancer cohorts.
    • The model outperformed strong clinical and molecular baselines in risk prediction.
    • Sequence-derived structural embeddings were shown to be superior to text-based biomedical embeddings.

    Conclusions:

    • GLLM provides an effective fusion strategy for enhancing node representations using gene expression and protein structure.
    • The framework supports personalized surveillance planning by identifying high-risk cancer patients.
    • GLLM's lightweight architecture allows for seamless integration into clinical oncology workflows.