Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs

doi:10.1093/bioinformatics/btab751

CSpace > 应用数学研究所

	Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs
	Leng, Jiacheng 1,2; Wu, Ling-Yun1,2
	2022-02-01
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	38 期号:3 页码:770-777
摘要	Motivation: Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. Results: Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method.
DOI	10.1093/bioinformatics/btab751
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020YFA0712402] ; National Natural Science Foundation of China[11631014]
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:000743386000021
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/59912
专题	应用数学研究所
通讯作者	Wu, Ling-Yun
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, MADIS,IAM, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Leng, Jiacheng,Wu, Ling-Yun. Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs[J]. BIOINFORMATICS,2022,38(3):770-777.
APA	Leng, Jiacheng,&Wu, Ling-Yun.(2022).Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs.BIOINFORMATICS,38(3),770-777.
MLA	Leng, Jiacheng,et al."Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs".BIOINFORMATICS 38.3(2022):770-777.