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

Abstract

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.