ArticleIUCrJ2019
Evaluation of the performance of classification algorithms for XFEL single-particle imaging data.
Article in IUCrJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Scalable 3D reconstruction for X-ray single particle imaging with online machine learning.Nature communications · 2025Article
- Article
- The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations.Journal of synchrotron radiation · 2023Article
- Article
- Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers.Journal of applied crystallography · 2022Article
- Noise reduction and mask removal neural network for X-ray single-particle imaging.Journal of applied crystallography · 2022Article
- GPU-accelerated multitiered iterative phasing algorithm for fluctuation X-ray scattering.Journal of applied crystallography · 2021Article
- An encryption-decryption framework to validating single-particle imaging.Scientific reports · 2021Article
- Selecting XFEL single-particle snapshots by geometric machine learning.Structural dynamics (Melville, N.Y.) · 2021Article
- An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser.IUCrJ · 2020Article
- The XFEL Protein Crystallography: Developments and Perspectives.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps.
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