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

Single particle imaging (SPI) is a promising method for native structure determination which has undergone a fast progress with the development of X-ray Free-Electron Lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus multiple hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient for training of the neural network. We demonstrate here that a convolutional neural network (CNN) can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.Comment: 23 pages, 6 figures, 3 table

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

Single particle imaging (SPI) is a promising method of native structure determination, which has undergone fast progress with the development of x-ray free-electron lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus non-single hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient to train the neural network. We demonstrate here that a convolutional neural network can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for diffracted intensity represented by color images on a linear scale using YOLOv2 for classification. It shows an accuracy of about 95% with precision and recall of about 50% and 60%, respectively, in comparison to manual data classification

Young Researchers Advancing Computational Science: Perspectives of the Young Scientists Conference 2015

We present an annual international Young Scientists Conference (YSC) on computational science http://ysc.escience.ifmo.ru/, which brings together renowned experts and young researchers working in high-performance computing, data-driven modeling, and simulation of large-scale complex systems. The first YSC event was organized in 2012 by the University of Amsterdam, the Netherlands and ITMO University, Russia with the goal of opening a dialogue on the present and the future of computational science and its applications. We believe that the YSC conferences will strengthen the ties between young scientists in different countries, thus promoting future collaboration. In this paper we briefly introduce the challenges the millennial generation is facing; describe the YSC conference history and topics; and list the keynote speakers and program committee members. This volume of Procedia Computer Science presents selected papers from the 4th International Young Scientists Conference on Computational Science held on 25 June − 3 July 2015 in Athens, Greece

An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser

An improved analysis for single-particle imaging (SPI) experiments, using the limited data, is presented here. Results are based on a study of bacteriophage PR772 performed at the Atomic, Molecular and Optical Science instrument at the Linac Coherent Light Source as part of the SPI initiative. Existing methods were modified to cope with the shortcomings of the experimental data: inaccessibility of information from half of the detector and a small fraction of single hits. The general SPI analysis workflow was upgraded with the expectation-maximization based classification of diffraction patterns and mode decomposition on the final virus-structure determination step. The presented processing pipeline allowed us to determine the 3D structure of bacteriophage PR772 without symmetry constraints with a spatial resolution of 6.9 nm. The obtained resolution was limited by the scattering intensity during the experiment and the relatively small number of single hits

Corrigendum: An advanced workflow for single-particle imaging with the limited data at an X-ray free-electron laser

An error in Fig. 3(c) of the article by Assalauova et al. [IUCrJ (2020), 7, 1102–1113] is corrected