Scholarlink

Abstract <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GeneLens: A Python Package Implementing Monte Carlo Machine Learning and Network Analysis Methods for Biomarker Discovery and Gene Functional Annotation

  • 期刊论文

  • 发表日期：

    2025年9月22日

查看全文
  • G. J. Osmak,
  • M. V. Pisklova

摘要

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. 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.