Real-time and decision taking selection of single-particles during automated cryo-EM sessions based on neuro-fuzzy method

作     者：Gil-Carton, David Zamora, Miguel Sutherland, James D. Barrio, Rosa Garrido, Izaskun Valle, Mikel Garrido, Aitor J. 

作者机构：Cooperat Ctr Res Biosci CIC BioGUNE Struct Biol Unit Bizkaia Technol Pk Derio 48160 Spain Univ Basque Country UPV EHU Dept Automat Control & Syst Engn ACG Bilbao 48013 Bizkaia Spain Cooperat Ctr Res Biosci CIC BioGUNE Funct Genom Unit Bizkaia Technol Pk Derio 48160 Spain 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2016年第55卷

页      面：403-416页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Spanish Ministry of Economy and Competitiveness [BFU2012-34873, BFU2014-52282-P] GIU14/07 DPI2015-70075-R IT987-16 

主　　题：Cryo-electron microscopy Decision support systems Fuzzy logic Image processing Single-particle analysis 

摘      要：Cryo-electron microscopy (cryo-EM) is a three-dimensional (3D) averaging technique that makes use of two-dimensional (2D) images of biological macromolecules preserved in a thin layer of vitreous ice. Recent advances in the field have facilitated the evolution of cryo-EM towards atomic resolution, and the technique provides 3D maps with detailed description of biological macromolecules. Data acquisition at the transmission electron microscope (TEM) is the first crucial step during the single-particle analysis workflow in cryo-EM. In order to exploit the potential of this structural technique for atomic or near atomic resolution, the initial collection must allow recording of large datasets and, hence, requires operating the TEM in automated mode. The quality of the acquired dataset relies, however, on the expertise of researchers and unsupervised operations might result in low data quality. This work presents the first expert system integrated in a novel scheme to automate cryo-EM data acquisition in a TEM. This development takes advantage of fuzzy logic systems to integrate the working mode of an expert in a linguistic manner and to learn from acquired data through an adaptive network. A new method based on different image-processing algorithms and on adaptive neuro-fuzzy inference systems (ANFIS) identifies, in an unsupervised manner, the single-particles present in cryo-EM images during the automated acquisition on a TEM. This single-particle identification system is integrated in a new intelligent control scheme to automate cryo-EM data acquisition. A classic fuzzy inference system (FIS) was programmed to make appropriate decisions during the session. The designed system can be trained for a specific sample and allows for unsupervised but efficient data collection imitating the working mode of an experienced microscopist. (C) 2016 Elsevier Ltd. All rights reserved.