用AI解码基因调控机制，可直接生成可验证的科学假设。
Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms
- 模仿中心法则构建神经网络，通过注意力机制解析基因调控
- 在5个基因剔除数据中预测扰动效果相关性达0.84，识别出关键调控元件
- 无需临床数据即可推断药物靶点的生物学效应，适合药理研究者
现有生物AI模型缺乏可解释性，其内部表征无法对应真实生物关系。理解基因调控需能直接探查学习结构并生成可实验验证假说的模型。CDT-II架构仿照中心法则：DNA自注意力、RNA自注意力及转录控制的跨注意力机制，仅需基因组嵌入与单细胞表达原始数据。应用于K562 CRISPRi数据（5个基因完全剔除），模型预测扰动效应均值相关系数r=0.84，恢复GFI1B调控网络（富集倍数6.6，P=3.5×10⁻¹⁷），且跨注意力聚焦于ENCODE调控元件（28个靶点平均富集7.67倍，P<0.001）。基于梯度的归因分析准确预测治疗靶点扰动的下游后果（均值r=0.82）。应用于TFRC（PPMX-T003抗体靶点），梯度分析揭示铁依赖性DNA合成、红细胞结构、氧化应激等通路——与Ⅰ期试验报告的贫血和网织红细胞减少、临床前研究显示的铁死亡一致，未输入任何临床数据，证明CDT-II可作为从扰动实验中揭示临床相关调控结构的AI显微镜。
原文摘要 · Abstract (English)
Current biological AI models lack interpretability -- their internal representations do not correspond to biological relationships that researchers can examine. Understanding gene regulation requires models whose learned structure can be directly interrogated to generate experimentally testable hypotheses. CDT-II mirrors the central dogma in its architecture -- DNA self-attention, RNA self-attention, and cross-attention for transcriptional control -- requiring only genomic embeddings and raw per-cell expression. Applied to K562 CRISPRi data with five genes held out entirely, CDT-II predicts perturbation effects (per-gene mean r = 0.84), recovers the GFI1B regulatory network (6.6-fold enrichment, P = 3.5 x 10^{-17}), and shows that cross-attention focuses on ENCODE regulatory elements including CTCF sites (mean 7.67x across 28 targets, P < 0.001). Gradient-based attribution accurately predicts downstream consequences of perturbing therapeutic targets (mean r = 0.82). Applied to TFRC, the target of the anti-TfR1 antibody PPMX-T003, gradient analysis identifies genes involved in erythrocyte structure, iron-dependent DNA synthesis, and oxidative stress -- pathways that align with anemia and reticulocyte decrease reported in Phase 1 trials and ferroptosis demonstrated in preclinical studies, without any clinical data as input, establishing CDT-II as an AI microscope that reveals clinically relevant regulatory structure from perturbation experiments alone.
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