Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms.

ABSTRACT: Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ. However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep l

SUBMITTER: Li R 

PROVIDER: S-EPMC7682871 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature