We present GeneLens, a Python package for comprehensive analysis of differentially expressed genes and biomarker discovery. The package consists of two core modules, FSelector for biomarker identification by utilizing Monte Carlo simulations of L1-regularized models and NetAnalyzer for functional prediction of selected gene sets based on the topology of their protein-protein interaction networks. FSelector includes: (1) automated gene selection through iterative bootstrap sampling, (2) calculation of gene significance weights by taking account of ROC-AUC models and their number in simulations, and (3) adaptive thresholding for feature space reduction. NetAnalyzer performs a pathway enrichment analysis while integrating the significance weights from FSelector. Implemented as a PIP module, GeneLens provides standardized algorithms for applying machine learning and network analysis methods in differential gene expression studies, along with automated model hyperparameter tuning and visualization tools.