Towards molecular structure discovery from cryo-ET density volumes via modelling auxiliary semantic prototypes is a research paper published in Briefings in Bioinformatics (2024). On theSindex it has a DataRank of 0. It has been cited 2 times.
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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: 2211597
Collaborative Research: III: Medium: Systematic De Novo Identification of Macromolecular Complexes in Cryo-Electron Tomography Images
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 Science Foundation
Grant: 2205148
Tools4Cells: Machine-learning aided morphodynamics characterization of stem cell differentiation using label-free microscopies
National Science Foundation
Grant: 2238093
CAREER: Cryo-electron tomography derived multiscale integrative modeling of subcellular organization
FWCI
0.72
Citation Percentile
0.8%
Citation Trend
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