KMS Of Academy of mathematics and systems sciences, CAS
Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery | |
Min, Wenwen1; Liu, Juan1; Zhang, Shihua2,3 | |
2018-05-01 | |
Source Publication | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS |
ISSN | 1545-5963 |
Volume | 15Issue:3Pages:944-953 |
Abstract | 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. |
Keyword | Sparse logistic regression network-regularized penalty survival risk prediction feature selection |
DOI | 10.1109/TCBB.2016.2640303 |
Language | 英语 |
Funding Project | 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 Research Area | Biochemistry & Molecular Biology ; Computer Science ; Mathematics |
WOS Subject | Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability |
WOS ID | WOS:000434295100024 |
Publisher | IEEE COMPUTER SOC |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/30458 |
Collection | 应用数学研究所 |
Affiliation | 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 |
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. |
Files in This Item: | ||||||
There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment