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Related Experiment Video

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Adaptive multi-omics integration framework for breast cancer survival analysis.

Esmaeil Hasanzadeh1, Nasrollah Moghadam Charkari2

  • 1Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Scientific Reports
|November 3, 2025
PubMed
Summary

This study integrates multi-omics data using genetic programming to identify breast cancer biomarkers. The approach improves survival analysis, offering insights into cancer progression and potential therapeutic strategies.

Keywords:
Breast cancerData integrationGenetic programmingMulti-omicsSurvival analysis

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Breast cancer is a significant global health challenge.
  • Novel prognostic and therapeutic strategies are needed.
  • Multi-omics data offers potential for deeper biological insights.

Purpose of the Study:

  • To integrate multi-omics data (genomics, transcriptomics, epigenomics) for breast cancer.
  • To identify molecular signatures driving progression and impacting survival.
  • To optimize integration and feature selection using genetic programming.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) multi-omics data.
  • Employed genetic programming for adaptive integration and feature selection.
  • Developed a three-component framework: preprocessing, GP-based integration/selection, and model development.

Main Results:

  • Achieved a concordance index (C-index) of 78.31 on the training set (5-fold CV).
  • Obtained a C-index of 67.94 on the test set.
  • Demonstrated improved breast cancer survival analysis through integrated multi-omics.

Conclusions:

  • Adaptive multi-omics integration shows promise for enhancing breast cancer survival analysis.
  • Highlights the importance of inter-layer molecular interactions.
  • Presents a flexible framework applicable to other cancer types.