Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms
文献类型：期刊论文
| 作者 | Yu, Liangyong1; Li, Ran2 ; Zeng, Xiangrui1; Wang, Hongyi3; Jin, Jie4; Ge, Yang4; Jiang, Rui2; Xu, Min1
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| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2021-01-15 |
| 卷号 | 37期号:2页码:185-191 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btaa671 |
| 通讯作者 | Xu, Min(mxu1@cs.cmu.edu) |
| 英文摘要 | Motivation: 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. Results: 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. |
| WOS关键词 | BIOLOGY |
| 资助项目 | U.S. National Institutes of Health (NIH)[P41GM103712] ; U.S. National Institutes of Health (NIH)[R01GM134020] ; U.S. National Science Foundation (NSF)[DBI-1949629] ; U.S. National Science Foundation (NSF)[IIS-2007595] ; Mark Foundation for Cancer Research grant[19-044-ASP] ; Carnegie Mellon University's Center for Machine Learning and Health |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000649439900006 |
| 出版者 | OXFORD UNIV PRESS |
| 资助机构 | U.S. National Institutes of Health (NIH) ; U.S. National Science Foundation (NSF) ; Mark Foundation for Cancer Research grant ; Carnegie Mellon University's Center for Machine Learning and Health |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45214]  |
| 专题 | 模式识别国家重点实验室_计算生物学与机器智能 |
| 通讯作者 | Xu, Min |
| 作者单位 | 1.Carnegie Mellon Univ, Computat Biol Dept, Pittsburgh, PA 15213 USA 2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 3.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Liangyong,Li, Ran,Zeng, Xiangrui,et al. Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms[J]. BIOINFORMATICS,2021,37(2):185-191. |
| APA | Yu, Liangyong.,Li, Ran.,Zeng, Xiangrui.,Wang, Hongyi.,Jin, Jie.,...&Xu, Min.(2021).Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms.BIOINFORMATICS,37(2),185-191. |
| MLA | Yu, Liangyong,et al."Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms".BIOINFORMATICS 37.2(2021):185-191. |
入库方式： OAI收割
来源：自动化研究所
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