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

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.