{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T14:24:56Z","timestamp":1769264696001,"version":"3.49.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T00:00:00Z","timestamp":1570147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31671386"],"award-info":[{"award-number":["31671386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91430112"],"award-info":[{"award-number":["91430112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971377"],"award-info":[{"award-number":["31971377"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Major Scientific and Technological Special Project for \u2018Significant New Drugs Development\u2019","award":["2018ZX09J18112"],"award-info":[{"award-number":["2018ZX09J18112"]}]},{"name":"Shanghai Supercomputer Center and the National Center for Protein Sciences Shanghai"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present a deep-learning segmentation model that employs fully convolutional networks trained with synthetic data of known 3D structures, called PARSED (PARticle SEgmentation Detector). Without using any experimental information, PARSED could automatically segment the cryo-EM particles in a whole micrograph at a time, enabling faster particle picking than previous template\/feature-matching and particle-classification methods. Applications to six large public cryo-EM datasets clearly validated its universal ability to pick macromolecular particles of various sizes. Thus, our deep-learning method could break the particle-picking bottleneck in the single-particle analysis, and thereby accelerates the high-resolution structure determination by cryo-EM.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The PARSED package and user manual for noncommercial use are available as Supplementary Material (in the compressed file: parsed_v1.zip).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz728","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T11:28:48Z","timestamp":1569497328000},"page":"1252-1259","source":"Crossref","is-referenced-by-count":31,"title":["Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules"],"prefix":"10.1093","volume":"36","author":[{"given":"Ruijie","family":"Yao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Genetic Engineering, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University , Shanghai 200438, China"}]},{"given":"Jiaqiang","family":"Qian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Genetic Engineering, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University , Shanghai 200438, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5238-1704","authenticated-orcid":false,"given":"Qiang","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Genetic Engineering, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University , Shanghai 200438, China"},{"name":"Multiscale Research Institute of Complex Systems, Fudan University , Shanghai 201203, China"}]}],"member":"286","published-online":{"date-parts":[[2019,10,4]]},"reference":[{"key":"2023013110123487300_btz728-B1","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1093\/bioinformatics\/btt429","article-title":"A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs","volume":"29","author":"Abrishami","year":"2013","journal-title":"Bioinformatics"},{"key":"2023013110123487300_btz728-B2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.tibs.2014.10.005","article-title":"How cryo-EM is revolutionizing structural biology","volume":"40","author":"Bai","year":"2015","journal-title":"Trends Biochem. 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