Network-Regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery

Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. However, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here, we first introduce a general regularized Logistic Regression (LR) framework with regularized term lambda parallel to omega parallel to(1) + eta omega(T) M omega, which can reduce to different penalties, including Lasso, elastic net, and network regularized terms with different M. This framework can be easily solved in a unified manner by a cyclic coordinate descent algorithm which can avoid inverse matrix operation and accelerate the computing speed. However, if those estimated omega(i) and omega(j), have opposite signs, then the traditional network-regularized penalty may not perform well. To address it, we introduce a novel network-regularized sparse LR model with a new penalty lambda parallel to omega parallel to(1) + eta vertical bar omega vertical bar(T) M vertical bar omega vertical bar to consider the difference between the absolute values of the coefficients. We develop two efficient algorithms to solve it. Finally, we test our methods and compare them with the related ones using simulated and real data to show their efficiency.