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Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks
Machine learning methods are used for an automated classification of experimental two-time X-ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard-type simulations of liqu...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9348880/ https://www.ncbi.nlm.nih.gov/pubmed/35974741 http://dx.doi.org/10.1107/S1600576722004435 |
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author | Timmermann, Sonja Starostin, Vladimir Girelli, Anita Ragulskaya, Anastasia Rahmann, Hendrik Reiser, Mario Begam, Nafisa Randolph, Lisa Sprung, Michael Westermeier, Fabian Zhang, Fajun Schreiber, Frank Gutt, Christian |
author_facet | Timmermann, Sonja Starostin, Vladimir Girelli, Anita Ragulskaya, Anastasia Rahmann, Hendrik Reiser, Mario Begam, Nafisa Randolph, Lisa Sprung, Michael Westermeier, Fabian Zhang, Fajun Schreiber, Frank Gutt, Christian |
author_sort | Timmermann, Sonja |
collection | PubMed |
description | Machine learning methods are used for an automated classification of experimental two-time X-ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard-type simulations of liquid–liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto-encoder network and a differential evolution based algorithm. The method presented here is a first step towards handling large amounts of dynamic data measured at high-brilliance synchrotron and X-ray free-electron laser sources, facilitating fast comparison with phase field models of phase separation. |
format | Online Article Text |
id | pubmed-9348880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-93488802022-08-15 Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks Timmermann, Sonja Starostin, Vladimir Girelli, Anita Ragulskaya, Anastasia Rahmann, Hendrik Reiser, Mario Begam, Nafisa Randolph, Lisa Sprung, Michael Westermeier, Fabian Zhang, Fajun Schreiber, Frank Gutt, Christian J Appl Crystallogr Research Papers Machine learning methods are used for an automated classification of experimental two-time X-ray photon correlation maps from an arrested liquid–liquid phase separation of a protein solution. The correlation maps are matched with correlation maps generated with Cahn–Hilliard-type simulations of liquid–liquid phase separations according to two simulation parameters and in the last step interpreted in the framework of the simulation. The matching routine employs an auto-encoder network and a differential evolution based algorithm. The method presented here is a first step towards handling large amounts of dynamic data measured at high-brilliance synchrotron and X-ray free-electron laser sources, facilitating fast comparison with phase field models of phase separation. International Union of Crystallography 2022-06-15 /pmc/articles/PMC9348880/ /pubmed/35974741 http://dx.doi.org/10.1107/S1600576722004435 Text en © Sonja Timmermann et al. 2022 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Timmermann, Sonja Starostin, Vladimir Girelli, Anita Ragulskaya, Anastasia Rahmann, Hendrik Reiser, Mario Begam, Nafisa Randolph, Lisa Sprung, Michael Westermeier, Fabian Zhang, Fajun Schreiber, Frank Gutt, Christian Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks |
title | Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks |
title_full | Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks |
title_fullStr | Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks |
title_full_unstemmed | Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks |
title_short | Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn–Hilliard-type simulations using auto-encoder networks |
title_sort | automated matching of two-time x-ray photon correlation maps from phase-separating proteins with cahn–hilliard-type simulations using auto-encoder networks |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9348880/ https://www.ncbi.nlm.nih.gov/pubmed/35974741 http://dx.doi.org/10.1107/S1600576722004435 |
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