RLassoCox

RLassoCox implements the RLasso-Cox model for accurate survival prediction and survival biomarker discovery by integrating gene interaction information into the Lasso-Cox model.

Key Features:

• Gene interaction integration: Incorporates gene interaction network information into the Lasso-Cox model framework.
• Random walk weighting: Uses random walk to evaluate the topological weight of genes in the gene interaction network.
• Topological importance highlighting: Emphasizes topologically important genes to improve the generalization ability of the Lasso-Cox model.
• Small gene set identification: Identifies small gene sets with high prognostic performance on independent datasets.

Scientific Applications:

• Survival prediction: Enables accurate survival prediction through integrated gene interaction and expression analysis.
• Survival biomarker discovery: Identifies robust survival biomarkers for various cancer types.
• Cancer prognosis: Identifies robust survival biomarkers applicable across independent cancer datasets.

Methodology:

The RLasso-Cox model uses random walk to evaluate the topological weight of genes in the gene interaction network, then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model.