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A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation.

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第一作者： Xiangrui,Zeng

第一单位： Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh 15213, USA.

作者： Xiangrui,Zeng ^[1] ; Miguel Ricardo,Leung ^[2] ; Tzviya,Zeev-Ben-Mordehai ^[2] ; Min,Xu ^[3]

作者单位： Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh 15213, USA. ^[1] Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Cryo-electron Microscopy, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands. ^[2] Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh 15213, USA. Electronic address: mxu1@cs.cmu.edu. ^[3]

关键词 Cellular electron cryo-tomography Convolutional autoencoder Convolutional neural network Deep learning Image semantic segmentation Machine learning Macromolecular complex Particle picking Pose normalization Structural pattern mining Subtomogram classification Unsupervised learning Visual proteomics Weakly supervised learning

医学主题词算法(Algorithms);冷冻电子显微镜检查(Cryoelectron Microscopy);图像处理, 计算机辅助(Image Processing, Computer-Assisted);大分子物质(Macromolecular Substances);软件(Software)

DOI 10.1016/j.jsb.2017.12.015

PMID 29289599

发布时间 2023-10-13

基金项目

060208/Z/00/Z/WT_/Wellcome Trust/United Kingdom 093305/Z/10/Z/WT_/Wellcome Trust/United Kingdom P41 GM103712/GM/NIGMS NIH HHS/United States 107578/Z/15/Z/WT_/Wellcome Trust/United Kingdom Wellcome Trust/United Kingdom

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