Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography
文献类型：期刊论文
| 作者 | Wang, Yao-Yu1,2; Wan, Xiao-Hua3; Chen, Cheng4; Zhang, Fa3; Cui, Xue-Feng4 |
| 刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2025-05-22 |
| 页码 | 13 |
| 关键词 | particle detection cryo-electron tomography pattern recognition deep learning |
| ISSN号 | 1000-9000 |
| DOI | 10.1007/s11390-025-4816-2 |
| 英文摘要 | Advances in cryo-electron tomography (cryo-ET) have enabled the visualization of molecules within their native cellular environments in three-dimensions (3D). These visualizations are essential for studying the functions of biological entities in their natural conditions. Recently, deep learning techniques have shown significant success in tackling the challenge of particle detection in cryo-ET data. However, accurately identifying and classifying multi-class molecules remain challenging due to factors like low signal-to-noise ratios and the wide range of particle sizes. In this study, we introduce a novel framework CFNPicker for 3D object detection applied to cryo-ET analysis. A major advantage of our method is the design of central feature network (CFN) to integrate central features across multiple scales, allowing for the accurate detection of both small (<= 200) and large (>= 600) molecules. Additionally, we propose an adaptive weighted sampling training strategy to distinguish the complex noise distribution in the background, reducing false positive particles. We also construct the localization label to explicitly utilize the size and position variations of multi-class protein structures. Compared with existing methods, CFN improves the F1 score for classification by 3.6%, 7.3%, 6.6%, and 5.1% for the four smallest molecules tested respectively, while preserving similar or higher F1 scores for other molecules analyzed. |
| 资助项目 | National Key Research and Development Program of China[2021YFF0704300] ; National Natural Science Foundation of China[32241027] ; National Natural Science Foundation of China[62072441] ; National Natural Science Foundation of China[62072283] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001492846400001 |
| 出版者 | SPRINGER SINGAPORE PTE LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42403]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhang, Fa; Cui, Xue-Feng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China 4.Shandong Univ, Sch Comp Sci & Technol, Qingdao 266237, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yao-Yu,Wan, Xiao-Hua,Chen, Cheng,et al. Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2025:13. |
| APA | Wang, Yao-Yu,Wan, Xiao-Hua,Chen, Cheng,Zhang, Fa,&Cui, Xue-Feng.(2025).Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,13. |
| MLA | Wang, Yao-Yu,et al."Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2025):13. |
入库方式： OAI收割
来源：计算技术研究所
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