{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deeppicker-a-deep-learning-approach-for-fully","title":"DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM","arxiv_id":"1605.01838","date":"2016-05-06","proceeding":null,"authors":["Feng Wang","Huichao Gong","Gaochao liu","Meijing Li","Chuangye Yan","Tian Xia","Xueming Li","Jianyang Zeng"],"abstract":"Particle picking is a time-consuming step in single-particle analysis and\noften requires significant interventions from users, which has become a\nbottleneck for future automated electron cryo-microscopy (cryo-EM). Here we\nreport a deep learning framework, called DeepPicker, to address this problem\nand fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker\nemploys a novel cross-molecule training strategy to capture common features of\nparticles from previously-analyzed micrographs, and thus does not require any\nhuman intervention during particle picking. Tests on the recently-published\ncryo-EM data of three complexes have demonstrated that our deep learning based\nscheme can successfully accomplish the human-level particle picking process and\nidentify a sufficient number of particles that are comparable to those manually\nby human experts. These results indicate that DeepPicker can provide a\npractically useful tool to significantly reduce the time and manual effort\nspent in single-particle analysis and thus greatly facilitate high-resolution\ncryo-EM structure determination.","url_abs":"http://arxiv.org/abs/1605.01838v1","url_pdf":"http://arxiv.org/pdf/1605.01838v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deeppicker-a-deep-learning-approach-for-fully","repo_url":"https://github.com/nejyeah/DeepPicker-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"single-particle-analysis","task_name":"Single Particle Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}