A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data | |
Min, Wenwen1; Liu, Juan1; Luo, Fei1; Zhang, Shihua2![]() | |
刊名 | IEEE TRANSACTIONS ON NANOBIOSCIENCE
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2016-06-01 | |
卷号 | 15期号:4页码:362-370 |
关键词 | L-0-penalized singular value decomposition (L-0-SVD) miRNA cluster miRNA-gene joint module multiple-output sparse group lasso |
ISSN号 | 1536-1241 |
DOI | 10.1109/TNB.2016.2556744 |
英文摘要 | 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. |
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 |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000381445900008 |
内容类型 | 期刊论文 |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/23608] ![]() |
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. |
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