Data · dataset · 2023
SHREC Cryo-ET 2021 Dataset: Classification in Cryo-Electron Tomograms
Listed in IISH Dataverse
Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions.
Description
Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyze cryo-electron tomograms.
To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure.
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In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryo-ET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM.
We also experimentally confirm that there is a negative relationship between particle size and performance for all methods. You can find more details in the related publication and on the contest webpage.
Links
Where it is published
- Dataverse dataset page dataverse.nl/dataset.xhtml?persistentId=doi%3A10.34894%2FXRTJMA ↗
landing page · from datasets iisg amsterdam
- DOI doi.org/10.34894/xrtjma ↗
DOI / persistent id · from datasets iisg amsterdam
Catalogue records · 1
- Dataverse API dataverse.nl/api/datasets/:persistentId/?persistentId=doi%3A10.34894%2FXRTJ… ↗
metadata API · from datasets iisg amsterdam
Topics
- Stated by source
- Medicine, Health and Life Sciences
- From keywords
- Life Sciences · Medicine & Health
Provenance · 1 source records, 11 field assertions
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|---|---|---|---|
| IISH Dataverse | doi:10.34894/XRTJMA | 13 d ago | JSON v1 |
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