Lasso Regression Model is primarily used for bioinformatics analysis.

Perform Cox survival analysis with Lasso penalized regression to automatically select gene features significantly associated with survival prognosis. The tool determines optimal regularization parameter λ through cross-validation to avoid overfitting, and constructs a risk score model using genes with non-zero coefficients. Suitable for prognostic marker selection in high-dimensional gene expression data. Outputs include selected gene list, regression coefficients, and risk scores for each sample for subsequent independent prognostic analysis.

Main Use Cases:

• Explore data patterns and support subsequent in-depth analysis

Key Features:

• Standardized processing and result export
• Key charts and table output
• Can be used as input for subsequent analysis