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 |
| 关键词 | Enhancer–promoter interactions Cell line Convolutional neural network Transfer learning Gradient reversal layer |
| DOI | 10.1186/s12859-020-03844-4 |
| 英文摘要 | AbstractBackgroundEnhancer–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.ResultsIn 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).ConclusionsSEPT 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. |
| 语种 | 英语 |
| 出版者 | BioMed Central |
| WOS记录号 | BMC:10.1186/S12859-020-03844-4 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/52111]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhang,Shao-Wu; Zhang,Shihua |
| 作者单位 | 1.Chinese Academy of Sciences; Center for Excellence in Animal Evolution and Genetics 2.Northwestern Polytechnical University; Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation 3.Chinese Academy of Sciences; NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science 4.University of Chinese Academy of Sciences; School of Mathematical Sciences |
| 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). |
| 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). |
| 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). |
入库方式： OAI收割
来源：数学与系统科学研究院
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