Academic Journal
Tomogram exploration through template matching and deep learning.
| Τίτλος: | Tomogram exploration through template matching and deep learning. |
|---|---|
| Συγγραφείς: | Coray R; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain. Electronic address: raffaele.coray@csic.es., Molina-Ribagorda A; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain., Castaño-Díez D; Instituto Biofisika (UPV/EHU, CSIC), University of the Basque Country, Leioa, Spain. Electronic address: daniel.castano@csic.es. |
| Πηγή: | Current opinion in structural biology [Curr Opin Struct Biol] 2026 Oct; Vol. 100, pp. 103319. Date of Electronic Publication: 2026 Jul 01. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Country of Publication: England NLM ID: 9107784 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-033X (Electronic) Linking ISSN: 0959440X NLM ISO Abbreviation: Curr Opin Struct Biol Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2003->: London : Elsevier Science Original Publication: London, UK : Current Biology, 1991- |
| Ιατρικοί όροι (MeSH): | Electron Microscope Tomography*/methods , Image Processing, Computer-Assisted*/methods , Cryoelectron Microscopy*/methods , Deep Learning* |
| Περίληψη: | Template matching (TM) is a long-standing tool in cryo-electron tomography (cryo-ET), where it has been applied to localize macromolecular complexes across a wide range of sample types. Recent algorithmic and computational advances have substantially increased the speed, accuracy, and practical resolution of the technique, enabling TM with fine angular sampling and voxel sizes that allow extraction of structural information beyond particle detection and toward exploration of higher-order spatial organization. In addition, deep learning (DL) approaches have emerged as powerful alternatives for particle picking and segmentation. Together, TM and DL form complementary components of modern cryo-ET workflows. This review discusses recent methodological advances in both approaches and highlights how they are increasingly integrated into cutting-edge pipelines for tomography exploration. (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Entry Date(s): | Date Created: 20260701 Date Completed: 20260908 Latest Revision: 20260908 |
| Update Code: | 20260909 |
| DOI: | 10.1016/j.sbi.2026.103319 |
| PMID: | 42385532 |
| Βάση Δεδομένων: | MEDLINE |
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