https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2020Cossio_CrossValidation 2026-05-24T19:36:10Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2020Cossio_CrossValidation&diff=3764&oldid=prev 2020-07-27T07:43:07Z

<p>Created page with &quot;== Citation == Cossio, P. Need for Cross-Validation of Single Particle Cryo-EM. Journal of chemical information and modeling, 2020, 60, 2413-2418 == Abstract == Cross-vali...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Cossio, P. Need for Cross-Validation of Single Particle Cryo-EM. Journal of chemical information and modeling, 2020, 60, 2413-2418 <br /> <br /> == Abstract ==<br /> <br /> Cross-validation is used to determine the validity of a model on unseen data by assessing if the model is overfitted to noise. It is widely used in many fields, from artificial intelligence to structural biology in X-ray crystallography and nuclear magnetic resonance. Although there are concerns of map overfitting in cryo-electron microscopy (cryo-EM), cross-validation is rarely used. The problem is that establishing a performance metric of the maps over unseen data (given by 2D-projection images) is difficult due to the low signal-to-noise ratios in the individual particles. Here, I present recent advances for cryo-EM map reconstruction. I highlight that the gold-standard procedure can fail to detect map overfitting in certain cases, showing the necessity of assessing the map quality on unbiased data. Finally, I describe the challenges and advantages of developing a robust cross-validation methodology for cryo-EM. <br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://pubs.acs.org/doi/10.1021/acs.jcim.9b01121<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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