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February 18, 2026Biomarkers in Medicine

Integrative multi-platform gene expression and machine learning analysis for glioblastoma biomarker discovery with experimental validation

Haseeb Nisar Ali Islam Amna Arshad Fatima Tu Zahra Saleh S. Alwahaishi Romena Qazi Irfan Yousaf qurat ul ain Tariq

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Key Points

The research aims to discover biomarkers for glioblastoma using an integrative machine learning approach.
Integrative analysis combining multiple gene expression platforms
Application of machine learning algorithms for biomarker identification
Experimental validation of identified biomarkers
Identification of specific biomarkers linked to glioblastoma
Demonstrated potential for improving personalized treatment strategies through biomarker discovery

Implication

Integrative analysis identifies glioblastoma biomarkers, suggesting new paths for personalized treatment strategies.

Abstract

This robust integrative machine learning approach for biomarker discovery in GBM supports the development of more personalized treatment strategies.

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Cite This Study

Nisar et al. (2026) studied this question.

synapsesocial.com/papers/69994b41873532290d01f6d4 https://doi.org/10.1080/17520363.2026.2631632

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Machine learning model on multi-omics data enables risk stratification and identifies molecular heterogeneity and therapeutic targets in glioblastomaOriginal Research · 2026 · 5 citations
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