Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium- resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. The architecture was also applied to experimentally-derived cryo- EM density regions of 571 protein chains. . The average F1 score for helix detection is 0.747 and 0.674 for β-sheets in a test involving seven cryo-EM density regions. Additionally, we extend an arc-length association method to β -strands and show that this method for measuring error is superior to many popular methods. An interactive tool is also presented that can visualize the results of this arc-length association method.
","abstract_html":"<p>Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium- resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. The architecture was also applied to experimentally-derived cryo- EM density regions of 571 protein chains. . The average F1 score for helix detection is 0.747 and 0.674 for β-sheets in a test involving seven cryo-EM density regions. Additionally, we extend an arc-length association method to β -strands and show that this method for measuring error is superior to many popular methods. An interactive tool is also presented that can visualize the results of this arc-length association method.</p>","abstract_has_math":false,"creators":["Haslam, Devin Reid"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Jing He","Mohammad Zubair","Desh Ranjan","Jiangwen Sun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-07-01T07:00:00Z","date_published":"2018-07-01T07:00:00Z","updated_at":"2026-07-24T03:34:11Z","subjects":["Convolutional neural network","Deep learning","Secondary Structures","Bioinformatics","Computer Sciences"],"languages":[],"rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
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"]},{"key":"dc:title","label":"Title","values":["Deep Learning for Segmentation Of 3D Cryo-EM Images"]}]}],"canonical_facts":{"dc:contributor":["Jing He","Mohammad Zubair","Desh Ranjan","Jiangwen Sun"],"dc:creator":["Haslam, Devin Reid"],"dc:date.available":["2018-10-05T07:00:00Z"],"dc:description.abstract":["Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium- resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. The architecture was also applied to experimentally-derived cryo- EM density regions of 571 protein chains. . The average F1 score for helix detection is 0.747 and 0.674 for β-sheets in a test involving seven cryo-EM density regions. Additionally, we extend an arc-length association method to β -strands and show that this method for measuring error is superior to many popular methods. An interactive tool is also presented that can visualize the results of this arc-length association method.
"],"dc:identifier":["9780438538405","https://digitalcommons.odu.edu/computerscience_etds/40"],"dc:rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
"],"dc:subject":["Convolutional neural network","Deep learning","Secondary Structures","Bioinformatics","Computer Sciences"],"dc:title":["Deep Learning for Segmentation Of 3D Cryo-EM Images"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T03:34:11Z"}