📍 Currently integrating microbiome + neuroimaging + machine learning to understand Parkinson's Disease and metabolic disorders.
- 🧠 Microbiome → host phenotype modeling (Parkinson’s disease, cognition, metabolism)
- 🧬 Metagenomics (16S / ITS / shotgun): preprocessing → functional profiling → interpretation
- 🤖 Machine learning pipelines for biomarker discovery (scikit-learn, PyTorch, Keras)
- �� Multi-omics integration (rs-fMRI + microbiome, longitudinal microbiome datasets)
Metagenomics & Bioinformatics
QIIME2 • MetaPhlAn • HUMAnN • MintTea • MOFA2 • PICRUSt2 • phyloseq • Gephi • Cytoscape
Machine Learning / Deep Learning
scikit-learn • PyTorch • Keras • XGBoost • SVM • Bayesian Networks
Programming
Python • R • Bash • SQL • Git • Docker
| Project | Description | Status |
|---|---|---|
| 🧠 PD-microbiome + rs-fMRI integration | ML workflow to predict cognitive impairment in Parkinson’s | Finalizing manuscript |
| 🦎 Axolotl limb regeneration (16S+ITS) | Network analysis & stage-specific microbiome shifts | Published (Wound Repair & Regeneration) |
| 🍽 Prediabetes longitudinal microbiome | Time-series modeling using MetaPhlAn & HUMAnN | Manuscript in prep |
| ❤️ HeartHelper (Google AI Academy) | Streamlit + FAISS + Gemini-powered digital health assistant | Live demo soon |
➡️ Repos will be uploaded gradually with reproducible notebooks & documentation.
- Metagenomics / microbiome bioinformatics
- Multi-omics integration (imaging, metabolomics, clinical metadata)
- ML for biomarker discovery or precision medicine
📧 delicebusranur@gmail.com
🔗 LinkedIn | 🔬 ResearchGate
✨ “I turn biological complexity into interpretable models.”