On Predicting Epithelial Mesenchymal Transition by Integrating RNA-binding Proteins and Correlation Data via L1/2-Regularization Method

Abstract

Identifying tumor metastasis signatures from gene expression data at the whole genome level remains an arduous challenge, particularly so when the number of genes is huge and the number of experimental samples is small. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than on tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. We apply an extended LASSO model, L1/2-regularization model, as a feature selector, to identify significant RNA-binding proteins (RBPs) that contribute to regulating the EMT. We find that the L1/2-regularization model significantly outperforms LASSO in the EMT regulation problem. Furthermore, remarkable improvement in L1/2-regularization model classification performance can be achieved by incorporating extra information, specifically correlation values. We demonstrate that the L1/2-regularization model is applicable for identifying significant RBPs in biological research. Identified RBPs will facilitate study of the underlying mechanisms of the EMT.