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

文献类型：期刊论文

作者Leng, Jiacheng1,2; Wu, Ling-Yun1,2
刊名BIOINFORMATICS
出版日期2022-02-01
卷号38期号:3页码:770-777
ISSN号1367-4803
DOI10.1093/bioinformatics/btab751
英文摘要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.
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
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:000743386000021
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59911]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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