Cancer Classification and Biomarker Selection Via a Penalized Logsum Network-based Logistic Regression Model
Overview
Biotechnology
Health Services
Authors
Affiliations
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.
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