- PII
- S30345553S0026898425050096-1
- DOI
- 10.7868/S3034555325050096
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume 59 / Issue number 5
- Pages
- 845-854
- Abstract
- 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.The FSelector includes: (1) automated gene selection through iterative bootstrap sampling; (2) calculation of gene significance weights taking into account ROC-AUC model performance and their number in simulations; (3) adaptive thresholding for feature space reduction. NetAnalyzer performs pathway enrichment analysis while integrating 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.
- Keywords
- транскриптомика машинное обучение Монте-Карло биомаркеры дифференциально экспрессирующиеся гены сетевой анализ
- Date of publication
- 31.01.2026
- Year of publication
- 2026
- Number of purchasers
- 0
- Views
- 55
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