Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network

doi:10.1186/s12859-020-03844-4

CSpace > 应用数学研究所

	Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network
	Jing, Fang 1; Zhang, Shao-Wu 1; Zhang, Shihua2,3,4
	2020-11-07
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	21 期号:1 页码:16
摘要	Background Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information. Results In this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer-promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves). Conclusions SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.
关键词	Enhancer– promoter interactions Cell line Convolutional neural network Transfer learning Gradient reversal layer
DOI	10.1186/s12859-020-03844-4
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[61873202] ; Natural Science Foundation of China[11661141019] ; Natural Science Foundation of China[61621003] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; National Ten Thousand Talent Program for Young Top-notch Talents ; Key Research Program of the Chinese Academy of Sciences[KFZD-SW-219] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000590701600003
出版者	BMC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/52471
专题	应用数学研究所
通讯作者	Zhang, Shao-Wu; Zhang, Shihua
作者单位	1.Northwestern Polytech Univ, Key Lab Informat Fus Technol, Minist Educ, Sch Automat, 127 West Youyi Rd, Xian 710072, Shaanxi, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, 55 Zhongguancun East Rd, Beijing 10090, Peoples R China 3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Jing, Fang,Zhang, Shao-Wu,Zhang, Shihua. Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network[J]. BMC BIOINFORMATICS,2020,21(1):16.
APA	Jing, Fang,Zhang, Shao-Wu,&Zhang, Shihua.(2020).Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network.BMC BIOINFORMATICS,21(1),16.
MLA	Jing, Fang,et al."Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network".BMC BIOINFORMATICS 21.1(2020):16.