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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Notes
- 1.
The EMPIAR indexes of these datasets are 10131, 10133, 10135, 10143, 10169, 10172 and 10173.
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Acknowledgments
This research is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences Grant (No. XDA19020400), the National Key Research and Development Program of China (No. 2017YFE0103900 and 2017YFA0504702), Beijing Municipal Natural Science Foundation Grant (No. L182053), the NSFC projects Grant (No. U1611263, U1611261 and 61672493), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase). This work is supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. This work is supported by U.S. National Science Foundation (NSF) grant DBI-1949629. XZ is supported by a fellowship from Carnegie Mellon University’s Center for Machine Learning and Health. And SG is supported by Postgraduate Study Abroad Program of National Construction on High-level Universities funded by China Scholarship Council.
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Gao, S. et al. (2020). Dilated-DenseNet for Macromolecule Classification in Cryo-electron Tomography. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_8
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