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1. Brain_Cancer_Gene_Expression_Analysis_for_Subtype_Classification_and_Biomarker_Discovery Brain_Cancer_Gene_Expression_Analysis_for_Subtype_Classification_and_Biomarker_Discovery Public

   This work focuses on developing a machine learning pipeline to classify brain cancer subtypes using the Brain_GSE50161 dataset (54,676 genes, 130 samples). We achieved 92.31% accuracy by reducing f...

   Jupyter Notebook 1 7

2. Machine_Learning_Based_Prediction_of_Cancer_Drug_Sensitivity_Using_Preclinical_GDSC_Dataset Machine_Learning_Based_Prediction_of_Cancer_Drug_Sensitivity_Using_Preclinical_GDSC_Dataset Public

   This project uses machine learning to model cancer drug response from the GDSC dataset, combining biological, experimental, and pathway features. Several ML algorithms were evaluated, with Random F...

   Jupyter Notebook

3. Transcriptomic-Dissection-of-CD34-Hematopoietic-Stem-Cells-for-Subtype-Specific-Gene-Signatures Transcriptomic-Dissection-of-CD34-Hematopoietic-Stem-Cells-for-Subtype-Specific-Gene-Signatures Public

   Computational analysis of gene expression profiles to uncover key biomarkers distinguishing bone marrow and peripheral blood CD34+ stem cells, with applications in diagnostics and stem cell transpl...

4. SMILES-2026-Signal-Project SMILES-2026-Signal-Project Public

   Signal Interference Cancellation for SMILES-2026. This Python solution uses Rank-1 SVD and Nonlinear modeling, outperforming the baseline by 141% (9.69 dB vs. 4.02 dB)

   Python 1