Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers

Assalauova, D.; Ignatenko, A.; Isensee, F.; Trofimova, D.; Vartanyants, I.A.

doi:10.1107/S1600576722002667

Journal of Applied Crystallography Journal of Applied
Crystallography

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Figure 7
PR772 virus reconstructed from the different data sets. (a)–(d) Reconstruction of single-hit diffraction patterns selected by MaxF1 with size filtering applied (a), (b) and MaxF1 with the EM algorithm and size filtering applied (c), (d). (e)–(h) Reconstruction of the single-hit diffraction patterns selected by moreSH with size filtering applied (e), (f) and moreSH with the EM algorithm and size filtering applied (g), (h). (a), (c) The 3D inner structure of the virus with 88% (brown), 75% (green) and 20% (grey) levels of intensity for the MaxF1 selections. (e), (g) The 3D inner structure of the virus with 86% (brown), 75% (green) and 20% (grey) levels of intensity for the moreSH selections. (b), (d), (f), (h) 2D slices of the corresponding structure with the same scale bar of 30 nm. For visual representation, each virus structure was upsampled three times.

JOURNAL OF
APPLIED
CRYSTALLOGRAPHY

ISSN: 1600-5767

Volume 55| Part 3| June 2022| Pages 444-454

https://doi.org/10.1107/S1600576722002667

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