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An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

文献类型：期刊论文

作者Jing, Fang1; Zhang, Shao-Wu1; Cao, Zhen2,3,4; Zhang, Shihua2,3,4
刊名IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
出版日期2021
卷号18期号:1页码:355-364
关键词Bioinformatics machine learning transcription factors binding sites convolutional neural networks DNA accessibility histone modification
ISSN号1545-5963
DOI10.1109/TCBB.2019.2901789
英文摘要Knowing 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.
资助项目National 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研究方向Biochemistry & Molecular Biology ; Computer Science ; Mathematics
语种英语
WOS记录号WOS:000615042600034
出版者IEEE COMPUTER SOC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/58140]  
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, 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
推荐引用方式
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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