Prediction of gene co-expression from chromatin contacts with graph attention network

doi:10.1093/bioinformatics/btac535

	Prediction of gene co-expression from chromatin contacts with graph attention network
	Zhang, Ke1,2 ; Wang, Chenxi3,4 ; Sun, Liping3 ; Zheng, Jie1,5
	2022-09-30
发表期刊	BIOINFORMATICS
ISSN	1367-4803
EISSN	1460-2059
发表状态	已发表
DOI	10.1093/bioinformatics/btac535
摘要	["Motivation: The technology of high-throughput chromatin conformation capture (Hi-C) allows genome-wide measurement of chromatin interactions. Several studies have shown statistically significant relationships between gene- gene spatial contacts and their co-expression. It is desirable to uncover epigenetic mechanisms of transcriptional regulation behind such relationships using computational modeling. Existing methods for predicting gene co-expression from Hi-C data use manual feature engineering or unsupervised learning, which either limits the prediction accuracy or lacks interpretability.","Results: To address these issues, we propose HiCoEx (Hi-C predicts gene co-expression), a novel end-to-end framework for explainable prediction of gene co-expression from Hi-C data based on graph neural network. We apply graph attention mechanism to a gene contact network inferred from Hi-C data to distinguish the importance among different neighboring genes of each gene, and learn the gene representation to predict co-expression in a supervised and task-specific manner. Then, from the trained model, we extract the learned gene embeddings as a model interpretation to distill biological insights. Experimental results show that HiCoEx can learn gene representation from 3D genomics signals automatically to improve prediction accuracy, and make the black box model explainable by capturing some biologically meaningful patterns, e.g., in a gene contact network, the common neighbors of two central genes might contribute to the co-expression of the two central genes through sharing enhancers."]
URL	查看原文
收录类别	SCI ; SCIE
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000838254400001
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/219679
专题	信息科学与技术学院_博士生 iHuman研究所_特聘教授组_Andrej Sali组生命科学与技术学院_博士生信息科学与技术学院_PI研究组_郑杰组
通讯作者	Zheng, Jie
作者单位	1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China 3.ShanghaiTech Univ, iHuman Inst, Shanghai 201210, Peoples R China 4.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 5.ShanghaiTech Univ, Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院; 上海科技大学
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Zhang, Ke,Wang, Chenxi,Sun, Liping,et al. Prediction of gene co-expression from chromatin contacts with graph attention network[J]. BIOINFORMATICS,2022.
APA	Zhang, Ke,Wang, Chenxi,Sun, Liping,&Zheng, Jie.(2022).Prediction of gene co-expression from chromatin contacts with graph attention network.BIOINFORMATICS.
MLA	Zhang, Ke,et al."Prediction of gene co-expression from chromatin contacts with graph attention network".BIOINFORMATICS (2022).