Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels.

ABSTRACT: Localizing macromolecules in crowded cellular cryo-electron tomography (cryo-ET) images or tomograms is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. In this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for tomograms. To achieve

SUBMITTER: Uddin MR 

PROVIDER: S-EPMC12661946 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature