[3dem] Looking for feedback on deep learning-based post-processing

[Paolo Swuec]

Dear CCPEMers and 3DEMers,

I hope this message finds you well. I am reaching out to seek your advice and perspective on a question that has been increasingly relevant in my work as a scientist as well as a reviewer. Specifically, I would greatly appreciate your feedback on how best to incorporate maps obtained through deep learning-based post-processing methods in SPA workflows.

While these approaches often produce visually improved maps (enhancing features such as connectivity and apparent resolution) I am particularly interested in understanding or even get your 2 cents on:


  *
Does anybody have data\maps showing clear signs of AI-hallucinations?
  *
If a paper includes map from deep learning-based post-processing method, must this be deposited on EMDB?
  *
What could be a reasonable validation tool to prove existence of new regions\loops derived from such maps and not seen in regularly post-processed maps? (We are lucky enough to have structural proteomics here, but what to do when this is not available?)

I am especially keen to hear about both positive experiences and any concerns or limitations you may have encountered in practice. I am NO expert at all in DL methods 😊 so please don’t shoot!

Thank you very much in advance for your time and insights. I believe this is an important topic for ensuring consistent and robust use of emerging computational methods within the field.

Best!

Paolo



 
Paolo Swuec
Head of National Facility for Structural Biology
Human Technopole
Palazzo Italia
Viale Rita Levi-Montalcini, 1
20157 Milan, Italy
T. +39 02-30247442
M. +39 3427758814
E. paolo.swuec at fht.org
humantechnopole.it
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