Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization.

ABSTRACT:

Motivation

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable differen

SUBMITTER: Bandyopadhyay H 

PROVIDER: S-EPMC10060710 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature