ShanghaiTech University Knowledge Management System
| CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy | |
| 2024-09-29 | |
| 状态 | 已发表 |
| 摘要 | In the past decade, deep conditional generative models have revolutionized the generation of realistic images, extending their application from entertainment to scientific domains. Single-particle cryo-electron microscopy (cryo-EM) is crucial in resolving near-atomic resolution 3D structures of proteins, such as the SARSCOV-2 spike protein. To achieve high-resolution reconstruction, a comprehensive data processing pipeline has been adopted. However, its performance is still limited as it lacks high-quality annotated datasets for training. To address this, we introduce physics-informed generative cryo-electron microscopy (CryoGEM), which for the first time integrates physics-based cryo-EM simulation with a generative unpaired noise translation to generate physically correct synthetic cryo-EM datasets with realistic noises. Initially, CryoGEM simulates the cryo-EM imaging process based on a virtual specimen. To generate realistic noises, we leverage an unpaired noise translation via contrastive learning with a novel mask-guided sampling scheme. Extensive experiments show that CryoGEM is capable of generating authentic cryoEM images. The generated dataset can used as training data for particle picking and pose estimation models, eventually improving the reconstruction resolution. |
| 语种 | 英语 |
| DOI | arXiv:2312.02235 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:86405083 |
| WOS类目 | Computer Science, Software Engineering |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/433530 |
| 专题 | 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_何旭明组 信息科学与技术学院_PI研究组_虞晶怡组 信息科学与技术学院_硕士生 信息科学与技术学院_本科生 |
| 通讯作者 | Zhang, Jiakai |
| 作者单位 | 1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Cellverse, Shanghai, Peoples R China 3.iHuman Inst, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Jiakai,Chen, Qihe,Zeng, Yan,et al. CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy. 2024. |
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