Systematic Biomarker Discovery for Glioblastoma Subtyping Using Machine Learning

We present a systematic pipeline for biomarker discovery in glioblastoma (GBM) subtyping. Using transcriptomics data, we integrate multiple feature selection strategies with gene co-expression network analysis to identify robust, stable biomarkers that reliably distinguish GBM subtypes. The approach is evaluated across multiple machine learning classifiers and demonstrates strong generalization and robustness.

Keywords: Glioblastoma, Biomarker Discovery, Transcriptomics, Machine Learning, Feature Selection, Gene Co-expression Networks, Computational Pathology

Recommended citation: Sutradhar, A., Banerjee, A., Samanta, A., Hossain, S. F., Dey, A., & Biswas, S. (2025). "Systematic Biomarker Discovery for Glioblastoma Subtyping Using Machine Learning." IEEE INDISCON 2025. pp. 1–6.
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