Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms

doi:10.1093/bioinformatics/btaa671

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 计算生物学与机器智能

	Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms
	Yu, Liangyong 1; Li, Ran 2; Zeng, Xiangrui 1; Wang, Hongyi 3; Jin, Jie4 ; Ge, Yang 4; Jiang, Rui 2; Xu, Min 1
发表期刊	BIOINFORMATICS
ISSN	1367-4803
	2021-01-15
卷号	37 期号:2 页码:185-191
通讯作者	Xu, Min(mxu1@cs.cmu.edu)
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.
DOI	10.1093/bioinformatics/btaa671
关键词[WOS]	BIOLOGY
收录类别	SCI
语种	英语
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
项目资助者	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
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000649439900006
出版者	OXFORD UNIV PRESS
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45215
专题	多模态人工智能系统全国重点实验室_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.