{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:53:38Z","timestamp":1767182018193,"version":"3.37.3"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"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\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P41GM103712","R01GM134020"],"award-info":[{"award-number":["P41GM103712","R01GM134020"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"NSF","doi-asserted-by":"publisher","award":["DBI-1949629","IIS-2007595"],"award-info":[{"award-number":["DBI-1949629","IIS-2007595"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014599","name":"Mark Foundation for Cancer Research","doi-asserted-by":"publisher","award":["19-044-ASP"],"award-info":[{"award-number":["19-044-ASP"]}],"id":[{"id":"10.13039\/100014599","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Carnegie Mellon University\u2019s Center for Machine Learning and Health"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Cryoelectron tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at submolecular resolution. Such information is critical for the accurate understanding of cellular processes. However, subtomogram classification remains one of the major challenges for the systematic recognition and recovery of the macromolecule structures in cryo-ET because of imaging limits and data quantity. Recently, deep learning has significantly improved the throughput and accuracy of large-scale subtomogram classification. However, often it is difficult to get enough high-quality annotated subtomogram data for supervised training due to the enormous expense of labeling. To tackle this problem, it is beneficial to utilize another already annotated dataset to assist the training process. However, due to the discrepancy of image intensity distribution between source domain and target domain, the model trained on subtomograms in source domain may perform poorly in predicting subtomogram classes in the target domain.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we adapt a few shot domain adaptation method for deep learning-based cross-domain subtomogram classification. The essential idea of our method consists of two parts: (i) take full advantage of the distribution of plentiful unlabeled target domain data, and (ii) exploit the correlation between the whole source domain dataset and few labeled target domain data. Experiments conducted on simulated and real datasets show that our method achieves significant improvement on cross domain subtomogram classification compared with baseline methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Software is available online https:\/\/github.com\/xulabs\/aitom.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa671","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T19:24:53Z","timestamp":1595445893000},"page":"185-191","source":"Crossref","is-referenced-by-count":9,"title":["Few shot domain adaptation for<i>in situ<\/i>macromolecule structural classification in cryoelectron tomograms"],"prefix":"10.1093","volume":"37","author":[{"given":"Liangyong","family":"Yu","sequence":"first","affiliation":[{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]},{"given":"Ran","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing 100084, China"}]},{"given":"Xiangrui","family":"Zeng","sequence":"additional","affiliation":[{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]},{"given":"Hongyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University , Beijing 100084, China"}]},{"given":"Jie","family":"Jin","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science , Beijing 100190, China"}]},{"given":"Yang","family":"Ge","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science , Beijing 100190, China"}]},{"given":"Rui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0881-5891","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"2023051511003499100_btaa671-B1","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/978-1-4899-0375-4_26","volume-title":"Neutrons in Biology","author":"Abola","year":"1984"},{"first-page":"176","year":"2018","author":"Alam","key":"2023051511003499100_btaa671-B2"},{"key":"2023051511003499100_btaa671-B3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.jsb.2008.02.008","article-title":"Classification and 3D averaging with missing wedge correction in biological electron tomography","volume":"162","author":"Bartesaghi","year":"2008","journal-title":"J. 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