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Related Experiment Video

Updated: May 5, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Integrated Multi-Omics and Machine Learning Framework Identifies Diagnostic Signatures and Druggable Targets in

Zifu Wang1, Jinqi Hou2, Yimin Chen1

  • 1School of Computing, Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Wilayah Persekutuan Kuala Lumpur, Malaysia.

Genes
|May 4, 2026
PubMed
Summary

This study identified CHEK1 as a key diagnostic gene for breast cancer (BC) using machine learning and causal inference. AI identified potential drug candidates targeting CHEK1, requiring further validation.

Keywords:
CHEK1breast cancerdrug repurposingmachine learningmulti-omics integration

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Breast cancer (BC) remains a leading cause of cancer mortality globally.
  • Limited availability of robust diagnostic biomarkers and targeted therapy options for BC.

Purpose of the Study:

  • To identify novel diagnostic biomarkers for breast cancer.
  • To discover potential therapeutic targets and compounds for breast cancer treatment.

Main Methods:

  • Integrated analysis of seven GEO datasets and TCGA-BRCA cohort transcriptomic data.
  • Applied differential gene expression (DEA), Weighted Gene Co-expression Network Analysis (WGCNA), and Protein-Protein Interaction (PPI) networks.
  • Developed an ensemble machine learning (ML) framework with 127 algorithms and SHAP analysis for hub gene identification.
  • Validated findings using single-cell transcriptome data, AI-assisted virtual screening, and molecular docking.

Main Results:

  • Identified CHEK1 and KIF23 as potential diagnostic genes with significant diagnostic potential (AUC: 0.625-0.938).
  • CHEK1 expression showed a causal relationship with BC genetic susceptibility (p_SMR < 0.05).
  • AI-assisted virtual screening prioritized 25 candidate compounds, with Olaparib and LY294002 showing favorable binding to CHEK1.

Conclusions:

  • CHEK1 is identified as a key diagnostic gene for breast cancer.
  • AI-driven drug repositioning and virtual screening identified potential CHEK1-targeting compounds.
  • Findings are in silico predictions requiring experimental validation for therapeutic application.