AI科学家系统助力高血压基因研究，全流程受控可复现。
NVAITC AI Scientist: A Governed End-to-End Research System -- A Hypertension GWAS Case Study
- 构建受控的智能科研系统，整合计划、执行与人类监督
- 在28万样本中复现关键高血压基因位点，最强信号达−log10(p)∼70
- 适合需要合规与可追溯性的医院科研团队使用
代理式科研系统正成为协调科学工作流的新范式，超越孤立的模型推理、代码生成或统计分析。但在机构级生物医学环境中部署，需具备研究规划、数据访问、工作流编排、证据追踪、可复现性及人工监督等受控机制。我们提出NVAITC AI Scientist（NAIS），一个受控的端到端代理科研系统，支持通用科学工作流，同时确保敏感数据留在机构隐私边界内。NAIS集成提案评审、执行规划、受控计算路由、可复现的工作流编排、证据生成及科学家介入监督。我们在真实世界高血压全基因组关联研究（GWAS）中验证了该系统，使用来自286,422名个体的医院关联基因型与电子健康记录（EHR）数据，遵循仅聚合数据政策。该代理规划了队列提取，编排了GWAS执行，生成了质控摘要，并撰写了面向发表的输出。人机联合审查发现了表型不一致，推动了高血压定义的迭代优化。校正后，代理编排的GWAS重现了已知高血压位点，包括FGF5、ATP2B1、CNNM2、FTO和GRB14，其中最强信号位于FGF5，−log10(p)∼70。作为二次演示，NAIS还支持药物性肝损伤预测工作流，多模态图神经网络AUC达0.842。结果表明，受控的代理科研系统可在保障合规的前提下，实现可比专家水平的生物医学发现。
原文摘要 · Abstract (English)
Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or statistical analysis. However, deployment in institutional biomedical environments requires governed mechanisms for research planning, data access, workflow orchestration, evidence tracking, reproducibility, and human oversight. We present NVAITC AI Scientist (NAIS), a governed end-to-end agentic research system designed to support domain-general scientific workflows while keeping protected data within institutional privacy boundaries. NAIS integrates proposal review, execution planning, governed computational routing, reproducible workflow orchestration, evidence generation, and scientist-in-the-loop oversight. We validate NAIS in a real-world hypertension genome-wide association study (GWAS) using hospital-linked genotype and electronic health record (EHR) data from 286,422 individuals under an aggregate-only data policy. The agent planned cohort extraction, orchestrated GWAS execution, generated quality-control summaries, and drafted publication-oriented outputs. Human-AI review identified phenotype discrepancies and enabled iterative refinement of the hypertension definition. After reconciliation, the agent-orchestrated GWAS reproduced established hypertension loci, including FGF5, ATP2B1, CNNM2, FTO, and GRB14, with the strongest signal at FGF5 reaching $-\log_{10}(p) \sim 70$. As a secondary demonstration, NAIS also supported a drug-induced liver injury prediction workflow, achieving a multimodal graph neural network AUC of 0.842. These results demonstrate that governed agentic research systems can support scalable AI-assisted biomedical discovery while producing outputs comparable to expert-led workflows.
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