Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery

doi:10.1109/TCBB.2016.2640303

CSpace > 应用数学研究所

	Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
	Min, Wenwen 1; Liu, Juan 1; Zhang, Shihua2,3
	2018-05-01
发表期刊	IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN	1545-5963
卷号	15 期号:3 页码:944-953
摘要	Molecular 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.
关键词	Sparse logistic regression network-regularized penalty survival risk prediction feature selection
DOI	10.1109/TCBB.2016.2640303
语种	英语
资助项目	National 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研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:000434295100024
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/30458
专题	应用数学研究所
通讯作者	Min, Wenwen
作者单位	1.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
推荐引用方式 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.