In the fight against cancer, identifying tumor cells among healthy tissue remains one of the most critical challenges in modern oncology. Our groundbreaking project, "Ghost Cell Busters," leverages cutting-edge machine learning and single-cell RNA sequencing to revolutionize how we detect and understand cancer at the cellular level.
Cancer diagnosis traditionally relies on tissue morphology and bulk molecular profiling, but these methods often miss the heterogeneity that drives treatment resistance. Single-cell RNA sequencing has opened new frontiers, but distinguishing tumor cells from normal cells in complex tissue samples remains computationally challenging.
We analyzed three comprehensive cancer datasets (GSE176078, GSE161529, GSE180286) to ensure our models generalize across different patient populations and experimental conditions. This cross-validation approach is crucial for developing clinically applicable tools.
We engineered novel cellular features including CNV scores, cell cycle signatures, apoptosis markers, and oncogene activation patterns to create comprehensive cellular fingerprints.
Using XGBoost and advanced ensemble methods, we developed interpretable models that not only classify cells but explain the biological reasoning behind each prediction.
We validated our predictions using orthogonal measures like genomic instability and performed comprehensive pathway analysis to identify therapeutic targets.
Integration with drug databases revealed actionable therapeutic targets, bridging the gap between computational discovery and clinical application.
Our models achieved remarkable accuracy by focusing on key oncogenes including MYC, CCND1, ERBB2, and CDK4. These genes showed consistent upregulation in predicted tumor cells across all datasets, providing robust biomarkers for cancer detection.
A critical breakthrough was using CNV (Copy Number Variation) scores as independent validation. Predicted tumor cells consistently showed higher genomic instability, confirming our models were capturing genuine cancer hallmarks rather than technical artifacts.
We visualized complex oncogenic networks including the PI3K–MAPK–EGFR axis, CDK4–CDK6–CCND1 pathway, and MYC signaling cascade. These network analyses revealed interconnected pathways driving tumor progression and identified multiple intervention points.
Our computational pipeline integrates state-of-the-art bioinformatics tools with advanced machine learning frameworks. The use of SHAP (SHapley Additive exPlanations) for model interpretation ensures that our predictions are not just accurate but also biologically meaningful and clinically interpretable.
This work represents a significant step toward precision oncology, where treatment decisions are guided by comprehensive cellular analysis rather than broad population averages. Our interpretable AI approach ensures that clinicians can understand and trust the computational predictions, accelerating adoption in clinical settings.
Dive into our comprehensive analysis, examine our code, and discover how AI is transforming cancer detection.
View on GitHub Browse Notebooks