https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Martinez_Review 2026-05-24T20:20:55Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2025Martinez_Review&diff=4995&oldid=prev 2025-06-20T10:26:54Z

<p>Created page with &quot;== Citation == A. Martinez-Sanchez, “Template matching and machine learning for cryo-electron tomography,” Current Opinion in Structural Biology, vol. 93, p. 103058, 2025. == Abstract == Cryo-electron tomography is the best-suited imaging technique for visual proteomics. Recent advances have increased the number, quality, and resolution of tomograms. However, object detection is the bottleneck task of the analysis workflow because, so far, only a few molecules can...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> A. Martinez-Sanchez, “Template matching and machine learning for cryo-electron tomography,” Current Opinion in Structural Biology, vol. 93, p. 103058, 2025.<br /> <br /> == Abstract ==<br /> <br /> Cryo-electron tomography is the best-suited imaging technique<br /> for visual proteomics. Recent advances have increased the<br /> number, quality, and resolution of tomograms. However, object<br /> detection is the bottleneck task of the analysis workflow<br /> because, so far, only a few molecules can be detected by<br /> computer methods for pattern recognition. This article introduces<br /> the major challenges in detecting molecular complexes<br /> for cryo-electron tomography. This paper also identifies<br /> the limitations of the current methods. Finally, it describes the<br /> approaches proposed to overcome these limitations.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S0959440X25000764<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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