Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms is a research paper published in PLoS Computational Biology (2020). On theSindex it has a DataRank of 0.416. It has been cited 15 times.
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Base Score Contribution
0.416
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: P41GM103712 and R01GM134020
National Science Foundation
Grant: DBI-1949629 and IIS-2007595
Mark Foundation
Grant: 19-044-ASP
NIGMS NIH HHS
Grant: P41 GM103712
NIGMS NIH HHS
Grant: R01 GM134020
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 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: 1F32GM013039-01
MOLECULAR ANALYSIS OF THE YEAST HEAT SHOCK TRANSCRIPTION
National Institutes of Health
Grant: 5P41GM103712-08
High Performance Computing for Multiscale Modeling of Biological Systems
Carnegie Mellon University (US), Center for Machine Learning and Health
King Abdullah University of Science and Technology
Tsinghua University
FWCI
0.85
Citation Percentile
0.8%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals