My role: Principal Investigator · System architect · Lead developer
Project Repository · Live System · Publications & Data · My GitHub
This repository provides an overview of the STING DSS research project. The full source code, deployment configuration, and technical documentation are maintained at the official project repository:
For publications, datasets, and citable outputs, visit the project website:
STING DSS is a full-stack clinical research platform I designed and built as part of TÜBİTAK 1001 Project No. 123E383 (2023–2026) at Süleyman Demirel University. It integrates five AI/ML methodologies into a single, sequential pipeline for pediatric ALL research — from raw drug–protein interaction data all the way to population-level synthetic patient cohort analysis.
The system is deployed as a production-ready, bilingual (TR/EN) web application accessible to the research team and collaborators worldwide.
Research leadership
- Conceived and directed the overall research programme
- Designed the five-layer methodological architecture connecting drug repositioning, ODE simulation, genetic algorithm optimisation, GNN digital twin prediction, and GAN synthetic patient generation
- Developed the unified 5-class risk stratification ontology harmonising NCI, COG, BFM, and SJCRH criteria
System development
- Architected and implemented the full-stack platform: FastAPI backend, React/Vite frontend, Docker Compose infrastructure
- Designed and implemented
TenDrugALLModel— a 48-dimensional ODE system modelling 10 drugs across 4 treatment phases with RK45 adaptive solver - Implemented GNN v2 (GCNConv×2, h=256) digital twin achieving median R² = 0.991 across 8 treatment outcome targets
- Designed the clean-schema CTGAN architecture eliminating label leakage (GAN-label concordance 0.41%), with post-hoc clinical enrichment pipeline
- Implemented all four XAI methods: SHAP KernelExplainer, Permutation Importance, Counterfactual search, and GEMEX
Open-source library
- Developed and published GEMEX v1.2.2 — a Riemannian manifold-based explainability library (
pip install gemex) used within the STING pipeline for geodesic sensitivity field analysis
Production deployment
- Deployed the system to a Natro VPS with Nginx reverse proxy, JWT authentication, bcrypt password hashing, slowapi rate limiting, and hardened API configuration
- Built bilingual Admin Panel with user management, activity logging, and TÜBİTAK research survey integration
Tab 1 Bi-LSTM Drug Repositioning 314,531 ligand–protein pairs (DrugBank · ChEMBL · PubChem · KIBA)
│ Copanlisib: −207.11 kcal/mol · Novobiocin: HSP90 inhibitor
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Tab 2 Patient & Treatment Setup 10 drugs · 4 phases · BSA · TPMT · Vitamin D · lifestyle factors
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Tab 3 ODE PK/PD Simulation TenDrugALLModel · 48-dim state · RK45 · 250 days
│ Sensitivity: Vitamin D 48.1% impact on WBC minimum
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Tab 4 GA Dose Optimisation BRR d8: 99.64% · EOI MRD: 3.3×10⁻⁵ · DNR: 149.2 mg/m²
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Tab 5 GNN Digital Twin + XAI Median R²: 0.991 · SHAP · Permutation · GEMEX · Counterfactual
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Tab 6 Synthetic Cohort (GAN) 200 patients · 5-class risk · COP+NOV: Lt→0.000 · WBC +17.5%
| Component | Detail |
|---|---|
| ODE engine | TenDrugALLModel — 48-dim state vector, 10 drugs, 4 phases, RK45 solver |
| GA optimisation | Population 80 · Generations 200 · 4 simultaneous clinical objectives |
| GNN architecture | GCNConv×2 (h=256, dropout=0.2) · heterogeneous patient graph · k=3 lag |
| CTGAN design | Clean-schema (30 static features) · post-hoc MRD/risk/PI/prognosis pipeline |
| XAI — GEMEX | Geodesic Sensitivity Field · Riemannian manifold · κ = 0.5612 (example patient) |
| Privacy validation | MIA AUC = 0.530 (≈ random) · clinical violation rate = 0% |
| Deployment | Docker Compose · Nginx · Redis · Celery · JWT · bcrypt · slowapi |
| Languages | Bilingual TR/EN · light/dark theme · session management |
Backend FastAPI · Python 3.10+ · PyTorch · SciPy (RK45) · SDV/CTGAN · SHAP · GEMEX v1.2.2
Frontend React 18 · Vite · Recharts · D3.js · Tailwind CSS
Infrastructure Docker Compose · Nginx · Redis · Celery · JWT · bcrypt · slowapi
| Name | Role |
|---|---|
| Prof. Dr. Utku Köse (me) · utkukose.com · ORCID | Principal Investigator / Developer — SDU / University of North Dakota / VelTech / Universidad Panamericana |
| Prof. Dr. Gözde Özkan Tükel | Researcher / Developer — Süleyman Demirel University |
| Assist. Prof. Dr. İlhan Uysal | Researcher / Developer — Burdur Mehmet Akif Ersoy University |
| Lect. Osman Ceylan | Researcher / Developer — Isparta Applied Sciences University |
| Lect. Emine Betül Sürücü | Researcher / Developer — Süleyman Demirel University |
Supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK), 1001 Programme, Project No: 123E383 (2023–2026), Süleyman Demirel University.
| Full source code & docs | github.com/tubitaksting |
| Live system | 37.148.208.183:8091 |
| Publications, datasets & citations | sting.sdu.edu.tr |
| GEMEX library (PyPI) | pypi.org/project/gemex |
| My GitHub profile | github.com/utkukose |
| My website | utkukose.com |
| ORCID | 0000-0002-9652-6415 |
STING DSS is a research prototype. Outputs have not been validated against real clinical data and must not be used for clinical decision-making. Apache 2.0 License.
This repository is maintained by Prof. Dr. Utku Köse as part of the STING project. For the full codebase, visit github.com/tubitaksting.