GNN optimization for Protein Interaction (PPI) analysis in Oncology. Implements normalized graph convolution for stable Edge AI execution on SBC
End-to-end computational platform for pharmaceutical-grade cancer biomarker discovery. Integrates multi-omics data, machine learning, and clinical validation frameworks for precision oncology applications.
Digital patient generation and drug response prediction via TCGA-DepMap integration — CVAE virtual patients, multi-level similarity scoring, 578-compound drug profiling
LLM-driven multi-agent simulation of the KRAS G12D pancreatic tumor microenvironment — emergent immunosuppression, NK/DC agents, ablation studies. bioRxiv 2026.
Cancer is calculable, geometric attractor framework. This is an OrganismCore derivative framework.
Edge-GNN: Constraint-aware graph neural networks for biological interaction modeling under edge deployment constraints (computational oncology, PPI networks).
Weighted combination of distance metrics on cancer mutation trees, optimized with survival data for prognostic prediction in AML and Breast Cancer.
Add a description, image, and links to the computational-oncology topic page so that developers can more easily learn about it.
To associate your repository with the computational-oncology topic, visit your repo's landing page and select "manage topics."