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Biol."],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Cryo\u2010electron microscopy (Cryo\u2010EM) and tomography (Cryo\u2010ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal\u2010to\u2010noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher\u2010quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a simulated annealing\u2010based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo\u2010ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experimented on simulated and experimental 2D Cryo\u2010EM images of Ribosome complex datasets respectively with SNR=10 and SNR=0.5, our method achieved better accuracy in terms of F\u2010measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>SA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo\u2010EM and 3D Cryo\u2010ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo\u2010EM and Cryo\u2010ET.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s40484-019-0191-8","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T04:51:26Z","timestamp":1583470286000},"page":"51-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A simulated annealing approach for resolution guided homogeneous cryo\u2010electron microscopy image selection"],"prefix":"10.1002","volume":"8","author":[{"given":"Jie","family":"Shi","sequence":"first","affiliation":[{"name":"<!--1--> Department of Computer Science The University of Hong Kong Hong Kong 999077 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangrui","family":"Zeng","sequence":"additional","affiliation":[{"name":"<!--2--> Computational Biology Department Carnegie Mellon University Pittsburgh PA 15213 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Jiang","sequence":"additional","affiliation":[{"name":"<!--3--> Department of Automation Tsinghua University Beijing 100084 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"<!--4--> Department of Computer Science and Engineering University of California\u2010Riverside Riverside CA 92521 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"<!--2--> Computational Biology Department Carnegie Mellon University Pittsburgh PA 15213 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,3]]},"reference":[{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"Frank J.(2006)Three\u2010dimensional Electron Microscopy of Macromolecular Assemblies.New York:Oxford University Press","DOI":"10.1093\/acprof:oso\/9780195182187.001.0001"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1083\/jcb.201304193"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature01513"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.str.2010.05.008"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-7744-1_2"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.str.2019.01.005"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2012.02.014"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-011-0012-5"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/51\/21\/015"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.03080"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty267"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1482"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.str.2011.12.007"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0033583509990059"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2006.05.009"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2012.09.006"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2007.11.007"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2010.03.011"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2004.02.006"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-72815-0_5"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrmicro.2016.7"},{"key":"e_1_2_8_23_2","doi-asserted-by":"publisher","DOI":"10.1561\/0600000009"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.02.065"},{"key":"e_1_2_8_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2003.10.024"},{"key":"e_1_2_8_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2003.11.005"},{"key":"e_1_2_8_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2003.10.025"},{"key":"e_1_2_8_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12900\u2010018\u20100094\u20103"},{"key":"e_1_2_8_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2009.06.011"},{"key":"e_1_2_8_30_2","volume-title":"Data Mining: Methods and Models","author":"Larose D. 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This article does not contain any studies with human or animal subjects performed by any of the authors.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethics Guidelines"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}