WikiSysop: Created page with "== Citation == Fan, Quanfu / Li, Yilai / Yao, Yuguang / Cohn, John / Liu, Sijia / Xu, Ziping / Vos, Seychelle / Cianfrocco, Michael. Cryorl: Reinforcement learning enables efficient cryo-em data collection. 2024. Proc. IEEE/CVF Winter Conf. on Applications of Computer Vision. p. 7892-7902 == Abstract == Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution s..."

2024-08-08T15:48:18Z

Created page with "== Citation == Fan, Quanfu / Li, Yilai / Yao, Yuguang / Cohn, John / Liu, Sijia / Xu, Ziping / Vos, Seychelle / Cianfrocco, Michael. Cryorl: Reinforcement learning enables efficient cryo-em data collection. 2024. Proc. IEEE/CVF Winter Conf. on Applications of Computer Vision. p. 7892-7902 == Abstract == Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution s..."

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== Citation ==

Fan, Quanfu / Li, Yilai / Yao, Yuguang / Cohn, John / Liu, Sijia / Xu, Ziping / Vos, Seychelle / Cianfrocco, Michael. Cryorl: Reinforcement learning enables efficient cryo-em data collection. 2024. Proc. IEEE/CVF Winter Conf. on Applications of Computer Vision. p. 7892-7902

== Abstract ==

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.

== Keywords ==

== Links ==

https://openaccess.thecvf.com/content/WACV2024/html/Fan_CryoRL_Reinforcement_Learning_Enables_Efficient_Cryo-EM_Data_Collection_WACV_2024_paper.html

== Related software ==

== Related methods ==

== Comments ==

2024Fan RL - Revision history