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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
Summary
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.
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.
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