Institute of Computing Technology, Chinese Academy IR
| SHREC 2020: Classification in cryo-electron tomograms | |
| Gubins, Ilja1; Chaillet, Marten L.2; van der Schot, Gijs2; Veltkamp, Remco C.1; Forster, Friedrich2; Hao, Yu3; Wan, Xiaohua3; Cui, Xuefeng4; Zhang, Fa3; Moebel, Emmanuel5; Wang, Xiao6; Kihara, Daisuke6,7; Zeng, Xiangrui8; Xu, Min8; Nguyen, Nguyen P.9; White, Tommi10; Bunyak, Filiz9 | |
| 2020-10-01 | |
| 发表期刊 | COMPUTERS & GRAPHICS-UK
 |
| ISSN | 0097-8493 |
| 卷号 | 91页码:279-289 |
| 摘要 | Cryo-electron tomography (cryo-ET) is an imaging technique that allows us to three-dimensionally visualize both the structural details of macro-molecular assemblies under near-native conditions and its cellular context. Electrons strongly interact with biological samples, limiting electron dose. The latter limits the signal-to-noise ratio and hence resolution of an individual tomogram to about 50 (5 nm). Biological molecules can be obtained by averaging volumes, each depicting copies of the molecule, allowing for resolutions beyond 4 (0.4 nm). To this end, the ability to localize and classify components is crucial, but challenging due to the low signal-to-noise ratio. Computational innovation is key to mine biological information from cryo-electron tomography. To promote such innovation, we provide a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in cryo-electron tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Six research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching, a traditional method widely used in cryo-ET research. We find that method performance correlates with particle size, especially noticeable for template matching which performance degrades rapidly as the size decreases. We learn that neural networks can achieve significantly better localization and classification performance, in particular convolutional networks with focus on high-resolution details such as those based on U-Net architecture. (C) 2020 Elsevier Ltd. All rights reserved. |
| 关键词 | Cryo-electron tomography Computer vision Pattern recognition Protein classification Benchmark |
| DOI | 10.1016/j.cag.2020.07.010 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | European Research Council under the European Union[724425 - BENDER] ; Nederlandse Organisatie voor Wetenschappelijke Onderzoek[Vici 724.016.001] ; Nederlandse Organisatie voor Wetenschappelijke Onderzoek[741.018.201] |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Software Engineering |
| WOS记录号 | WOS:000577434300023 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/15679 |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Gubins, Ilja |
| 作者单位 | 1.Univ Utrecht, Dept Informat & Comp Sci, Utrecht, Netherlands 2.Univ Utrecht, Dept Chem, Utrecht, Netherlands 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples R China 5.Inria Rennes Bretagne Atlantique, Rennes, France 6.Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA 7.Purdue Univ, Computat Biol Dept, W Lafayette, IN 47907 USA 8.Carnegie Mellon Univ, Computat Biol Dept, Pittsburgh, PA 15213 USA 9.Univ Missouri, Dept Elect Engn & Comp Sci, Columbia, MO 65211 USA 10.Univ Missouri, Dept Biochem, Columbia, MO USA |
| 推荐引用方式 GB/T 7714 | Gubins, Ilja,Chaillet, Marten L.,van der Schot, Gijs,et al. SHREC 2020: Classification in cryo-electron tomograms[J]. COMPUTERS & GRAPHICS-UK,2020,91:279-289. |
| APA | Gubins, Ilja.,Chaillet, Marten L..,van der Schot, Gijs.,Veltkamp, Remco C..,Forster, Friedrich.,...&Bunyak, Filiz.(2020).SHREC 2020: Classification in cryo-electron tomograms.COMPUTERS & GRAPHICS-UK,91,279-289. |
| MLA | Gubins, Ilja,et al."SHREC 2020: Classification in cryo-electron tomograms".COMPUTERS & GRAPHICS-UK 91(2020):279-289. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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