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Title: Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms
Macromolecular structure classification from cryo-electron tomography (cryo-ET) data is important for understanding macro-molecular dynamics. It has a wide range of applications and is essential in enhancing our knowledge of the sub-cellular environment. However, a major limitation has been insufficient labelled cryo-ET data. In this work, we use Contrastive Self-supervised Learning (CSSL) to improve the previous approaches for macromolecular structure classification from cryo-ET data with limited labels. We first pretrain an encoder with unlabelled data using CSSL and then fine-tune the pretrained weights on the downstream classification task. To this end, we design a cryo-ET domain-specific data-augmentation pipeline. The benefit of augmenting cryo-ET datasets is most prominent when the original dataset is limited in size. Overall, extensive experiments performed on real and simulated cryo-ET data in the semi-supervised learning setting demonstrate the effectiveness of our approach in macromolecular labeling and classification.  more » « less
Award ID(s):
1949629 2007595
PAR ID:
10430543
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Frontiers in Physiology
Volume:
13
ISSN:
1664-042X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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