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Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
Min, Wenwen1; Liu, Juan1; Zhang, Shihua2,3
2018-05-01
Source PublicationIEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN1545-5963
Volume15Issue:3Pages:944-953
AbstractMolecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. However, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here, we first introduce a general regularized Logistic Regression (LR) framework with regularized term lambda parallel to omega parallel to(1) + eta omega(T) M omega, which can reduce to different penalties, including Lasso, elastic net, and network regularized terms with different M. This framework can be easily solved in a unified manner by a cyclic coordinate descent algorithm which can avoid inverse matrix operation and accelerate the computing speed. However, if those estimated omega(i) and omega(j), have opposite signs, then the traditional network-regularized penalty may not perform well. To address it, we introduce a novel network-regularized sparse LR model with a new penalty lambda parallel to omega parallel to(1) + eta vertical bar omega vertical bar(T) M vertical bar omega vertical bar to consider the difference between the absolute values of the coefficients. We develop two efficient algorithms to solve it. Finally, we test our methods and compare them with the related ones using simulated and real data to show their efficiency.
KeywordSparse logistic regression network-regularized penalty survival risk prediction feature selection
DOI10.1109/TCBB.2016.2640303
Language英语
Funding ProjectNational Science Foundation of China[61379092] ; National Science Foundation of China[61422309] ; National Science Foundation of China[61621003] ; National Science Foundation of China[11661141019] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; Outstanding Young Scientist Program of CAS ; Key Research Program of Frontier Sciences, CAS[QYZDB-SSW-SYS008] ; Natural Science Foundation of Jiangsu Province[BK20161249] ; Open Research Funds of the State Key Laboratory of Software Engineering (SKLSE) ; Key Laboratory of Random Complex Structures and Data Science, CAS
WOS Research AreaBiochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS SubjectBiochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS IDWOS:000434295100024
PublisherIEEE COMPUTER SOC
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30458
Collection应用数学研究所
Affiliation1.Wuhan Univ, Sch Comp, State Key Lab Software Engn, Wuhan 430072, Hubei, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Min, Wenwen,Liu, Juan,Zhang, Shihua. Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2018,15(3):944-953.
APA Min, Wenwen,Liu, Juan,&Zhang, Shihua.(2018).Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,15(3),944-953.
MLA Min, Wenwen,et al."Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 15.3(2018):944-953.
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