A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules

doi:10.1093/bioinformatics/btr206

CSpace > 应用数学研究所

	A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
	Zhang, Shihua1,2 ; Li, Qingjiao 1,3; Liu, Juan 3; Zhou, Xianghong Jasmine 1
	2011-07-01
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	27 期号:13 页码:I401-I409
摘要	Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA-gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes. Results: Here we propose an effective data integration framework to identify the miRNA-gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA-gene interactions, and gene-gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics.
DOI	10.1093/bioinformatics/btr206
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:000291752600049
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/11268
专题	应用数学研究所
通讯作者	Zhang, Shihua
作者单位	1.Univ So Calif, Program Mol & Computat Biol, Los Angeles, CA 90089 USA 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Wuhan Univ, Sch Comp Sci, Wuhan 430079, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Shihua,Li, Qingjiao,Liu, Juan,et al. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules[J]. BIOINFORMATICS,2011,27(13):I401-I409.
APA	Zhang, Shihua,Li, Qingjiao,Liu, Juan,&Zhou, Xianghong Jasmine.(2011).A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules.BIOINFORMATICS,27(13),I401-I409.
MLA	Zhang, Shihua,et al."A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules".BIOINFORMATICS 27.13(2011):I401-I409.