Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?

Researchers introduce SCENE, a multi-agent AI framework that transforms general biomedical knowledge into specific, evidence-supported hypotheses grounded in experimental data. The system successfully identifies patient subgroups with different treatment responses in clinical trials and context-specific biological responses in genomic studies, bridging the gap between broad theoretical knowledge and actionable dataset-specific insights.

The research addresses a fundamental challenge in biomedical science: the disconnect between general domain knowledge and specific experimental findings. SCENE's bi-level architecture treats knowledge contextualization as an iterative search problem, with an upper level converting broad biomedical principles into dataset-aligned search directions and a lower level executing multi-objective optimization to find concrete, evidence-supported propositions. This approach enables domain experts to inspect and validate hypotheses systematically.

The framework reflects growing recognition that AI systems must bridge interpretability and evidence in high-stakes domains. Rather than relying solely on pattern recognition from data or applying generic knowledge without validation, SCENE creates a structured dialogue between prior knowledge and empirical evidence. This methodology aligns with broader trends toward explainable AI in healthcare, where stakeholder confidence depends on transparent reasoning.

The demonstrated applications show significant practical value. In clinical trial analysis, SCENE identifies specific patient subgroups likely to benefit from treatments, directly supporting personalized medicine initiatives. In LINCS L1000 genomic studies, the system identifies perturbational contexts with strong biological validity, accelerating drug discovery pipelines. Both applications require balancing statistical rigor with mechanistic plausibility—a persistent challenge in computational biology.

The framework's success suggests demand for AI systems that enhance rather than replace domain expertise. As biomedical research increasingly generates massive datasets, tools that contextualize knowledge while maintaining scientific interpretability become valuable infrastructure for research institutions, pharmaceutical companies, and clinical organizations seeking data-driven yet explainable insights.

• →SCENE framework converts general biomedical knowledge into specific, testable hypotheses grounded in actual experimental data.
• →Bi-level multi-agent architecture iteratively refines knowledge by balancing evidential strength with dataset support.
• →Clinical trial validation shows SCENE identifies patient subgroups with heterogeneous treatment responses better than baseline methods.
• →System produces fully inspectable and traceable hypotheses enabling expert validation before resource-intensive follow-up studies.
• →Framework addresses critical gap between theoretical knowledge and dataset-specific evidence in biomedical research.