ArticleBioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference)2020
Dilated-DenseNet For Macromolecule Classification In Cryo-electron Tomography.
Article in Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Medicinal plant leaf disease classification using optimal weighted features with dilated adaptive DenseNet and attention mechanism.Scientific reports · 2025Article
- High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization.Bioinformatics (Oxford, England) · 2022Article
- FSCC: Few-Shot Learning for Macromolecule Classification Based on Contrastive Learning and Distribution Calibration in Cryo-Electron Tomography.Frontiers in molecular biosciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Cryo-electron tomography (cryo-ET) combined with subtomogram averaging (STA) is a unique technique in revealing macromolecule structures in their near-native state. However, due to the macromolecular structural heterogeneity, low signal-to-noise-ratio (SNR) and anisotropic resolution in the tomogram, macromolecule classification, a critical step of STA, remains a great challenge. In this paper, we propose a novel convolution neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification in STA. The proposed 3D-Dilated-DenseNet is challenged by the synthetic dataset in the SHREC contest and the experimental dataset, and compared with the SHREC-CNN (the state-of-the-art CNN model in the SHREC contest) and the baseline 3D-DenseNet. The results showed that 3D-Dilated-DenseNet significantly outperformed 3D-DenseNet but 3D-DenseNet is well above SHREC-CNN. Moreover, in order to further demonstrate the validity of dilated convolution in the classification task, we visualized the feature map of 3D-Dilated-DenseNet and 3D-DenseNet. Dilated convolution extracts a much more representative feature map.
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Registered trials
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