Explainable machine learning for diffraction patterns

Serial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respe...

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Detalles Bibliográficos
Autores principales: Nawaz, Shah, Rahmani, Vahid, Pennicard, David, Setty, Shabarish Pala Ramakantha, Klaudel, Barbara, Graafsma, Heinz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
Materias:
Research Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543671/
https://www.ncbi.nlm.nih.gov/pubmed/37791364
http://dx.doi.org/10.1107/S1600576723007446

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543671/
https://www.ncbi.nlm.nih.gov/pubmed/37791364
http://dx.doi.org/10.1107/S1600576723007446

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