Cancer Research • Data Analytics • Biochemistry
Turning messy biological data into clean, reproducible insights 🧪📊
- 🧬 Cancer research: patient stratification, target discovery, pathway-level interpretation
- 📊 Data analytics: clean pipelines, reproducibility, modeling & storytelling
- 🧪 Biochemistry: proteins, mechanisms of action, drug–target interactions
🧠 Current vibe: integrating biological databases (DrugBank/UniProt), extracting targets, and connecting them to patient-level findings.
✨ Click to open my research toolkit
- 🧾 DrugBank, UniProt, Ensembl, NCBI, HGNC
- 🧬 Omics formats: FASTA, GTF, VCF (and many CSVs with feelings)
- 🧠 Methods: clustering, enrichment, pathway analysis, target mapping
✅ Want a quick tour? Click one:
🧩 “Choose your quest” (click me!)
- 🧬 If you're here for cancer research, check my patient stratification repos.
- 🧪 If you're here for biochemistry, look for drug–target and protein-centric analyses.
- 📊 If you're here for data analytics, open the dashboards and reproducible pipelines.
💬 Ask me about: drug targets, biomarkers, omics pipelines, R/Python reproducibility.
PS: if you scrolled this far, you deserve a cookie 🍪