CSpace  > 应用数学研究所
An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning
Jing, Fang1; Zhang, Shao-Wu1; Cao, Zhen2,3,4; Zhang, Shihua2,3,4
2021
Source PublicationIEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN1545-5963
Volume18Issue:1Pages:355-364
AbstractKnowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary DNA sequence. In addition to DNA sequences, histone modifications and chromatin accessibility are also important factors influencing their activity. They have been explored to predict TFBSs recently. However, current methods rarely take into account histone modifications and chromatin accessibility using CNN in an integrative framework. To this end, we developed a general CNN model to integrate these data for predicting TFBSs. We systematically benchmarked a series of architecture variants by changing network structure in terms of width and depth, and explored the effects of sample length at flanking regions. We evaluated the performance of the three types of data and their combinations using 256 ChIP-seq experiments and also compared it with competing machine learning methods. We find that contributions from these three types of data are complementary to each other. Moreover, the integrative CNN framework is superior to traditional machine learning methods with significant improvements.
KeywordBioinformatics machine learning transcription factors binding sites convolutional neural networks DNA accessibility histone modification
DOI10.1109/TCBB.2019.2901789
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61873202] ; National Natural Science Foundation of China[61473232] ; National Natural Science Foundation of China[11661141019] ; National 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 Research AreaBiochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS SubjectBiochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS IDWOS:000615042600034
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/58141
Collection应用数学研究所
Corresponding AuthorZhang, Shao-Wu; Zhang, Shihua
Affiliation1.Northwestern Polytech Univ, Sch Automat, Key Lab Informat Fus Technol, Minist Educ, Xian 710072, Shaanxi, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, Beijing 100190, 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
Recommended Citation
GB/T 7714
Jing, Fang,Zhang, Shao-Wu,Cao, Zhen,et al. An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2021,18(1):355-364.
APA Jing, Fang,Zhang, Shao-Wu,Cao, Zhen,&Zhang, Shihua.(2021).An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,18(1),355-364.
MLA Jing, Fang,et al."An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 18.1(2021):355-364.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jing, Fang]'s Articles
[Zhang, Shao-Wu]'s Articles
[Cao, Zhen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jing, Fang]'s Articles
[Zhang, Shao-Wu]'s Articles
[Cao, Zhen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jing, Fang]'s Articles
[Zhang, Shao-Wu]'s Articles
[Cao, Zhen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.