CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation.

ABSTRACT: Cryo-electron tomography (Cryo-ET) has been regarded as a revolution in structural biology and can reveal molecular sociology. Its unprecedented quality enables it to visualize cellular organelles and macromolecular complexes at nanometer resolution with native conformations. Motivated by developments in nanotechnology and machine learning, establishing machine learning approaches such as classification, detection and averaging for Cryo-ET image analysis has inspired broad interest. Yet, deep learning-based methods for biomedical imaging typically require large labeled datasets for good results, which can be a great challenge due to the expense of obtaining and labeling training data. To deal with this problem, we propose a generative model to simulate Cryo-ET images efficiently and reliab

SUBMITTER: Wu X 

PROVIDER: S-EPMC8970048 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature