Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.

ABSTRACT: Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which relies on cross-correlating tomograms with known structural templates. Current deep learning-based methods improve accuracy but require labor-intensive annotated datasets for supervised training. Here, we present Template Learning, a technique that combines deep learning accuracy with the convenience of training on biomolecular templates via domain randomization. Template Learning automates synthetic dataset generation, modeling molecular crowding, structural variability, and data acquisition variation, thereby reducing or eliminating the need f

SUBMITTER: Harastani M 

PROVIDER: S-EPMC12494793 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature