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

文献类型：期刊论文

作者Jing, Fang2; Zhang, Shao-Wu2; Zhang, Shihua1,3,4
刊名BMC BIOINFORMATICS
出版日期2020-11-07
卷号21期号:1页码:16
ISSN号1471-2105
关键词Enhancer– promoter interactions Cell line Convolutional neural network Transfer learning Gradient reversal layer
DOI10.1186/s12859-020-03844-4
英文摘要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.
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
语种英语
出版者BMC
WOS记录号WOS:000590701600003
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/52470]  
专题应用数学研究所
通讯作者Zhang, Shao-Wu; Zhang, Shihua
作者单位1.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
2.Northwestern Polytech Univ, Key Lab Informat Fus Technol, Minist Educ, Sch Automat, 127 West Youyi Rd, Xian 710072, Shaanxi, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, 55 Zhongguancun East Rd, Beijing 10090, 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.

入库方式： OAI收割

来源：数学与系统科学研究院

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