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

The oncodatasets package provides a curated collection of oncological, clinical trial, and cancer survival datasets for data analysis, statistical modeling, and machine learning research.

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.

Graphical Presentation of Clinical Data in Oncology Studies Using R

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

depa_oncology_scenario

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.

Global open source platform to fight cancer

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.

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.

Implementation of a Bioinformatics Strategy Applied to Clear Cell Renal Cell Carcinoma (ccRCC)

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.