Daily Arxiv

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CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis

Created by
  • Haebom

Author

Runmin Jiang, Genpei Zhang, Yuntian Yang, Siqi Wu, Minhao Wu, Wanyue Feng, Yizhou Zhao, Xi Xiao, Xiao Wang, Tianyang Wang, Xingjian Li, Muyuan Chen, Min Xu

CryoCCD: A Unified Framework for Cryo-EM Synthetic Data Generation

Outline

CryoCCD is a synthetic data generation framework developed to address the lack of high-quality annotated data for single-molecule cryo-electron microscopy (cryo-EM) analysis. This framework effectively reproduces the complex noise and biological heterogeneity of real-world images by integrating various biophysical modeling and a cryo-EM-specific conditional cycle-consistent diffusion model. CryoCCD generates structurally faithful micrographs, improves particle selection and pose estimation, and outperforms existing methods.

Takeaways, Limitations

Takeaways:
Various biophysical modeling for accurate description of biological tissues.
A diffusion model using cycle consistency and mask-guided contrastive learning to maintain structural fidelity.
Improved particle selection and pose estimation performance.
Effective generalization to new protein families.
Limitations:
There is no Limitations specified in the paper.
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