Personal Cancer Genome Reporter (PCGR)

Targeted and non-targeted anticancer drugs and drug regimens

A python framework for creating image-guided cancer patient digital twins.

Digital Twins for Computational Oncology

Graphical Presentation of Clinical Data in Oncology Studies Using R

Fusion Oncology fuses XGBoost drug-sensitivity models with DNABERT-2 genomic embeddings, then routes predictions through digital twin simulation, PK/PD pharmacokinetics, GNN scoring, and Bayesian uncertainty to produce confidence-scored companion diagnostic reports.

depa_oncology_scenario

Privacy-preserving healthcare AI for global oncology research. Features policy-gated federated learning, HIPAA/GDPR compliance evidence, and a comprehensive research dashboard.

pandas practice with (fake) oncology data

This is a project for research and analyzes the tolerance and actions limit of the radiotherapy point of view.

Pediatric brain tumor classification from RNA-seq data. Random Forest classifier achieves 82.1% accuracy across HGG, LGG, and Medulloblastoma subtypes on 1,791 samples. UCSD Carter Lab, Summer 2025.

This repository features a high-integrity machine learning pipeline developed to assist clinical researchers in stratifying patient risk for lung cancer. By utilizing an optimized Logistic Regression framework and UMAP, the project emphasizes model interpretability—a critical requirement for clinical validation and regulatory transparency.

Oncogeriatric synthetic data generator for research purposes.

Global open source platform to fight cancer

GCE framework for interpretable modeling in cardiac sarcoma survival using machine and deep learning. It captures complex feature relationships to enhance predictive insights and clinical understanding.