WikiSysop: Created page with "== Citation == Ni, S., Yang, C., Liu, Y., Zhang, Y., Shi, Y., Qian, A., Kong, R. and Chang, S. 2025. GTpick: A Deep Neural Network for Cryo-EM Particle Detection. Computational and Structural Biotechnology Journal. (2025). == Abstract == Accurate identification of protein particles in cryo-electron microscopy (Cryo-EM) images is crucial for achieving high-resolution three-dimensional (3D) structural reconstruction. However, this task faces multiple challenges, includi..."

2025-11-05T23:31:11Z

Created page with "== Citation == Ni, S., Yang, C., Liu, Y., Zhang, Y., Shi, Y., Qian, A., Kong, R. and Chang, S. 2025. GTpick: A Deep Neural Network for Cryo-EM Particle Detection. Computational and Structural Biotechnology Journal. (2025). == Abstract == Accurate identification of protein particles in cryo-electron microscopy (Cryo-EM) images is crucial for achieving high-resolution three-dimensional (3D) structural reconstruction. However, this task faces multiple challenges, includi..."

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== Citation ==

Ni, S., Yang, C., Liu, Y., Zhang, Y., Shi, Y., Qian, A., Kong, R. and Chang, S. 2025. GTpick: A Deep Neural Network for Cryo-EM Particle Detection. Computational and Structural Biotechnology Journal. (2025).

== Abstract ==

Accurate identification of protein particles in cryo-electron microscopy (Cryo-EM) images is crucial for achieving
high-resolution three-dimensional (3D) structural reconstruction. However, this task faces multiple challenges,
including low signal-to-noise ratios, densely distributed particles, and class imbalance. To address these issues,
this study proposes a target detection algorithm named GTpick, built upon the DETR framework. GTpick introduces
a cross-attention mechanism to enhance the interaction between target queries and specific image
features. In addition, a grouped one-to-many label assignment strategy is employed to improve recall in densely
populated regions, and a Focal Loss function is incorporated to mitigate the adverse effects of background noise
and class imbalance on detection accuracy. Experiments on large-scale Cryo-EM datasets demonstrate that
GTpick outperforms existing machine learning-based particle-picking methods in terms of the resolution of 3D
density maps reconstructed from detected particles and achieves superior Recall and F1 scores, particularly
excelling in the Recall metric.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S2001037025004337

== Related software ==

== Related methods ==

== Comments ==

2025Ni GTPick - Revision history