Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining
文献类型：期刊论文
| 作者 | Yang, Zhidong2,4; Li, Hongjia5; Zang, Dawei2; Han, Renmin1; Zhang, Fa3 |
| 刊名 | JOURNAL OF COMPUTATIONAL BIOLOGY
 |
| 出版日期 | 2024-05-28 |
| 页码 | 12 |
| 关键词 | cryo-EM image denoising noise simulation and deep learning |
| ISSN号 | 1066-5277 |
| DOI | 10.1089/cmb.2024.0513 |
| 英文摘要 | Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis. |
| 资助项目 | National Natural Science Foundation of China[61932018] ; National Natural Science Foundation of China[32241027] ; National Natural Science Foundation of China[62072280] ; National Natural Science Foundation of China[62072441] ; National Natural Science Foundation of China[61902373] ; National Key Research and Development Program of China[2021YFF0704300] ; Natural Science Foundation of Shandong Province[ZR2023YQ057] ; Youth Innovation Promotion Association CAS, the Foundation of the Chinese Academy of Sciences, China[JCPYJJ 22013] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB24050300] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB44030300] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:001234379300001 |
| 出版者 | MARY ANN LIEBERT, INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/40026]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Han, Renmin; Zhang, Fa |
| 作者单位 | 1.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Qingdao 266237, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing, Peoples R China 3.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Purdue Univ, Weldon Sch Biomed Engn, W Lafayette, IN USA |
| 推荐引用方式 GB/T 7714 | Yang, Zhidong,Li, Hongjia,Zang, Dawei,et al. Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2024:12. |
| APA | Yang, Zhidong,Li, Hongjia,Zang, Dawei,Han, Renmin,&Zhang, Fa.(2024).Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining.JOURNAL OF COMPUTATIONAL BIOLOGY,12. |
| MLA | Yang, Zhidong,et al."Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining".JOURNAL OF COMPUTATIONAL BIOLOGY (2024):12. |
入库方式： OAI收割
来源：计算技术研究所
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