A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data

doi:10.1109/TNB.2016.2556744

CSpace > 应用数学研究所

	A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data
	Min, Wenwen 1; Liu, Juan 1; Luo, Fei 1; Zhang, Shihua2
	2016-06-01
发表期刊	IEEE TRANSACTIONS ON NANOBIOSCIENCE
ISSN	1536-1241
卷号	15 期号:4 页码:362-370
摘要	MicroRNAs (miRNAs) are a class of small non-coding RNAs, which play key roles in gene regulation. Previous studies have revealed that they are likely to work together to regulate their common target genes. Discovery of the underlying combinatorial regulation between miRNAs and genes can provide useful information for understanding their functions as well as their close relevance to cancer. In this paper, we propose a two-stage method for identifying miRNA-gene regulatory modules by integrating miRNA and mRNA expression profiles and miRNA genomic cluster data. We first develop a multiple-output sparse group lasso (MSGL) regression model to predict a miRNA-gene association matrix (i.e., a miRNA-gene regulatory network). Further, we propose a L-0-regularized singular value decomposition (L-0-SVD) to identify miRNA-gene joint modules from the predicted regulatory network. We test our method on the matched miRNA and mRNA expression profiles in breast cancer from TCGA project and identify ten miRNA-gene regulatory modules. We find that 1) the modules are significantly associated in the predicted miRNA-gene regulatory network; 2) the modules are significantly enriched in GO biological processes and KEGG pathways, respectively; 3) many miRNAs and genes in the modules are related with breast cancer. On average, 51% of the miRNAs and 30% of the genes are related with breast cancer. The results demonstrate that miRNA-gene regulatory modules provide insights into the mechanisms of the combinatorial regulation between miRNAs and genes.
关键词	L-0-penalized singular value decomposition (L-0-SVD) miRNA cluster miRNA-gene joint module multiple-output sparse group lasso
DOI	10.1109/TNB.2016.2556744
语种	英语
资助项目	National Science Foundation of China[61402340] ; National Science Foundation of China[61379092] ; National Science Foundation of China[61422309] ; National Science Foundation of China[61171007] ; Natural Science Foundation of Hubei Province of China[2014CFB194] ; CAS ; Key Laboratory of Random Complex Structures and Data Science, CAS
WOS研究方向	Biochemistry & Molecular Biology ; Science & Technology - Other Topics
WOS类目	Biochemical Research Methods ; Nanoscience & Nanotechnology
WOS记录号	WOS:000381445900008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/23609
专题	应用数学研究所
通讯作者	Liu, Juan; Zhang, Shihua
作者单位	1.Wuhan Univ, Sch Comp, State Key Lab Software Engn, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Min, Wenwen,Liu, Juan,Luo, Fei,et al. A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data[J]. IEEE TRANSACTIONS ON NANOBIOSCIENCE,2016,15(4):362-370.
APA	Min, Wenwen,Liu, Juan,Luo, Fei,&Zhang, Shihua.(2016).A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data.IEEE TRANSACTIONS ON NANOBIOSCIENCE,15(4),362-370.
MLA	Min, Wenwen,et al."A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data".IEEE TRANSACTIONS ON NANOBIOSCIENCE 15.4(2016):362-370.