论文标题
冷冻：增强学习启用有效的冷冻EM数据收集
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection
论文作者
论文摘要
单粒子冷冻电子显微镜（Cryo-EM）已成为主流结构生物学技术之一，因为它具有确定动态生物分子的高分辨率结构的能力。但是，Cryo-EM数据获取仍然昂贵且富有劳动力，需要实质性的专业知识。结构生物学家需要一种更有效，更客观的方法来在有限的时间范围内收集最佳数据。我们将Cryo-EM数据收集任务制定为这项工作中的优化问题。目的是最大化指定期间拍摄的好图像的总数。我们表明，强化学习提供了一种有效的方法来计划冷冻EM数据收集，并成功地导航了异质的低温EM网格。我们开发的方法Cryorl证明了在类似设置下数据收集数据的平均用户的性能要好。
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of dynamic bio-molecules. However, cryo-EM data acquisition remains expensive and labor-intensive, requiring substantial expertise. Structural biologists need a more efficient and objective method to collect the best data in a limited time frame. We formulate the cryo-EM data collection task as an optimization problem in this work. The goal is to maximize the total number of good images taken within a specified period. We show that reinforcement learning offers an effective way to plan cryo-EM data collection, successfully navigating heterogenous cryo-EM grids. The approach we developed, cryoRL, demonstrates better performance than average users for data collection under similar settings.
