Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms is a research paper published in Frontiers in Physiology (2022). On theSindex it has a DataRank of 0. It has been cited 5 times.
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National Institutes of Health
Grant: R01GM134020 R01DK124219 R21DA052419 R21DA052419 P41GM103712 K01MH123896
National Science Foundation
Grant: DBI-1949629 IIS-2007595
U.S. Department of Defense
Grant: 192466
Mark Foundation For Cancer Research
Grant: 19-044-ASP
National Institutes of Health
Grant: 5P41GM103712-08
High Performance Computing for Multiscale Modeling of Biological Systems
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: 5R01DK124219-03
Novel dopaminergic mechanisms of islet hormone secretion and antipsychotic drug-induced metabolic disturbances
National Institutes of Health
Grant: 5R21DA052419-02
Ultra-fast high-resolution imaging of whole mouse brain for the study of drug addiction
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
National Institutes of Health
Grant: 5K01MH123896-02
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders
Center for Machine Learning and Health, School of Computer Science, Carnegie Mellon University
FWCI
0.92
Citation Percentile
0.8%
Citation Trend
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