Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

被引:12
作者
Zhou, Zhiming [1 ]
Huang, Haihui [1 ,2 ]
Liang, Yong [3 ,4 ]
机构
[1] Macau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China
[2] Shaoguan Univ, Shaoguan, Guangdong, Peoples R China
[3] Macau Univ Sci & Technol, Macau Inst Syst Engn, Macau, Peoples R China
[4] Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau, Peoples R China
基金
中国国家自然科学基金;
关键词
Regularization; gene selection; log-sum penalty; network-based knowledge; VARIABLE SELECTION; GENE-EXPRESSION; INTEGRATIVE ANALYSIS; REGULARIZATION; IDENTIFICATION; LASSO;
D O I
10.3233/THC-218026
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.
引用
收藏
页码:S287 / S295
页数:9
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