SCTA：一种用于从单细胞RNA测序中稳定且可解释地发现靶基因的智能体框架
SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing
- Novartis Biomedical Research（诺华生物医学研究公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究旨在从scRNA-seq数据中稳定且可解释地发现靶基因，提出SCTA智能体框架，将靶标发现分解为专门智能体并结合结构化证据约束推理，通过遗传性慢性胰腺炎研究验证其能提高靶标发现的多项性能。
AI中文摘要：
从单细胞RNA测序（scRNA-seq）数据中识别治疗靶基因在转化生物学中仍然是一项基本挑战。与批量检测不同，scRNA-seq能捕获异质细胞状态和稀有亚群，但这种异质性使得靶标发现对整个流程中的分析选择高度敏感。现有工作流程和通用分析智能体常产生不稳定或难以解释的靶标假设。我们提出SCTA，一个以决策为中心的智能体框架，用于从scRNA-seq数据中稳定且可解释地发现靶基因。它将靶标发现分解为与单细胞流程关键决策点对齐的专门智能体，并用结构化生物学证据约束下游推理。在一项关于遗传性慢性胰腺炎的代表性消融研究中，我们证明SCTA的全证据整合在测试配置中能在独立运行间产生最稳定的靶标选择，同时恢复先前研究中验证的生物学上连贯、与疾病相关的机制。这些结果表明，针对单细胞分析结构定制的决策感知智能体编排可提高精准医学中靶标发现的稳健性、可解释性和实际效用。
英文摘要：
Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous cellular states and rare subpopulations, but this same heterogeneity makes target discovery highly sensitive to analytical choices throughout the pipeline, including preprocessing, cell population selection, differential expression analysis, and downstream biological interpretation. As a result, existing workflows and general-purpose analysis agents often produce unstable or difficult-to-interpret target hypotheses, limiting their reliability for disease-focused discovery. We present SCTA (Single-Cell Target Agent), a decision-centric agentic framework for stable and interpretable target gene discovery from scRNA-seq data. Rather than treating analysis as a single general-purpose reasoning task, SCTA decomposes target discovery into specialized agents aligned with key decision points in the single-cell pipeline and constrains downstream reasoning with structured biological evidence. In a representative ablation study on hereditary chronic pancreatitis, we demonstrate that SCTA's full evidence integration yields the most stable target selection across independent runs among the tested configurations, while recovering biologically coherent, disease-relevant mechanisms validated in prior studies. These results suggest that decision-aware agent orchestration tailored to the structure of single-cell analysis can improve the robustness, interpretability, and practical utility of target discovery in precision medicine.
