该文章介绍了一种名为 META-SiM 的新型基础模型，它是一个基于 Transformer 的模型，旨在提高单分子时间追踪数据中生物发现的效率。单分子荧光显微镜 (SMFM) 可以揭示重要的生物学见解，但识别罕见但关键的中间体通常需要耗时的人工检查；META-SiM 通过在各种 SMFM 分析任务 上进行预训练，解决了这一挑战，其性能可与顶尖算法相媲美。该模型通过生成可封装详细信息的嵌入，使得通过 META-SiM Projector 这个在线工具实现高效的整数据集可视化和探索。结合 局部香农熵 (LSE) 指标，META-SiM 能够快速识别特定于条件的罕见行为，并通过在一个现有的剪接数据集上发现一个先前未被检测到的中间状态，证明了其加速生物发现的潜力。References: Li J, Zhang L, Johnson-Buck A, et al. Foundation model for efficient biological discovery in single-molecule time traces[J]. Nature Methods, 2025: 1-12.
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