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Machine Learning-Based Analysis of Large-Scale Transcriptomic Data Identifies Core Genes Associated with Multi-Drug

Yanwen Wang1, Fa Si1, Lei Huang1

  • 1College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

International Journal of Molecular Sciences
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study identifies core genes across 82 drug categories to predict drug resistance using machine learning. Findings offer new strategies for drug resistance intervention and novel drug development.

Keywords:
cellular omicsdrug resistance mechanismsfeature importancegene and pathway function

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Area of Science:

  • Genomics
  • Computational Biology
  • Pharmacology

Background:

  • Drug resistance poses a significant threat to therapeutic effectiveness.
  • Transcriptomics and machine learning are key tools for analyzing resistance.
  • Current research often lacks broad applicability across drug categories.

Purpose of the Study:

  • To systematically analyze transcriptomic data for drug resistance across diverse drug categories.
  • To identify core genes associated with drug resistance using machine learning.
  • To develop a predictive model for drug resistance.

Main Methods:

  • Systematic analysis of transcriptomic data from resistant cell lines treated with 1738 drugs (82 categories).
  • Integrated analysis using three classical machine learning methods to identify core genes.
  • Protein-protein interaction (PPI) network analysis and pathway enrichment analysis.

Main Results:

  • Identified core genes crucial for predicting drug resistance, validated using salinomycin.
  • Developed a high-accuracy resistance prediction model.
  • Discovered potential resistance mechanisms through pathway analysis.

Conclusions:

  • The identified core genes are valuable for predicting drug resistance across various drug classes.
  • This approach provides a new perspective for exploring cross-category resistance mechanisms.
  • Highlights directions for resistance intervention strategies and novel drug development.