Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization is a research paper published in Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 10 times.
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U.S. National Institutes of Health
Grant: R01GM134020
U.S. National Institutes of Health
Grant: P41GM103712
U.S. National Science Foundation
Grant: DBI-1949629
U.S. National Science Foundation
Grant: IIS-2007595
Mark Foundation For Cancer Research
Grant: 19-044-ASP
National Science Foundation
Grant: 2007595
III: Small: Improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning
National Institutes of Health
Grant: 5P41GM103712-08
High Performance Computing for Multiscale Modeling of Biological Systems
National Science Foundation
Grant: 1949629
IIBR Informatics: Reducing the training data annotation cost for learning-based macromolecule identification in cellular electron cryo-tomography
National Institutes of Health
Grant: 1R01GM134020-01A1
Novel machine learning approaches for improving structural discrimination in cryo-electron tomography
AMD COVID-19 HPC
Center of Machine Learning and Health at Carnegie Mellon University
Fields of Study
MeSH Terms
Keywords
Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization