Network-Based Logistic Classification with an Enhanced L1/2 Solver Reveals Biomarker and Subnetwork Signatures for Diagnosing Lung Cancer

Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic research. Recently, we gained a large amount of molecular interaction information about the disease-related biological processes and gathered them through various databases, which focused on many aspects of biological systems. In this paper, we use an enhanced L1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L1/2 net, where the predictors are based on gene-expression data with biologic network knowledge. Extensive simulation studies showed that our proposed approach outperforms L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches in terms of classification accuracy and stability. Furthermore, we applied our method for lung cancer data analysis and found that our method achieves higher predictive accuracy than L1 regularization, the old L1/2 penalized solver, and the Elastic net approaches, while fewer but informative biomarkers and pathways are selected