Abstract
Cryo-electron tomography (Cryo-ET) has made possible the observation of cellular organelles and macromolecular complexes at nanometer resolution in native conformations. Without disrupting the cell, Cryo-ET directly visualizes both known and unknown structures in situ and reveals their spatial and organizational relationships. Consequently, structural pattern mining (a.k.a. visual proteomics) needs to be performed to detect, identify and recover different sub-cellular components and their spatial organization in a systematic fashion for further biomedical analysis and interpretation. This chapter presents three major Cryo-ET structural pattern mining approaches to give an overview of traditional methods and recent advances in Cryo-ET data analysis. Template-based, supervised deep learning-based and template-free approaches are introduced in detail. Examples of recent biological and medical applications and future perspectives are provided.
| Original language | English (US) |
|---|---|
| Title of host publication | Computational Biology |
| Publisher | Codon Publications |
| Pages | 175-186 |
| Number of pages | 12 |
| ISBN (Print) | 9780994438195 |
| DOIs | |
| State | Published - Dec 10 2019 |
Bibliographical note
KAUST Repository Item: Exported on 2021-07-27Acknowledged KAUST grant number(s): BAS/1/1624, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26
Acknowledgements: This work was supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. XZ was supported by a fellowship from Carnegie Mellon University’s Center for Machine Learning and Health. XG acknowledges the support by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. BAS/1/1624, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, and FCC/1/1976-26.