Samsun N.

From expression data to biomarkers you can explain, defend, and build on. I’m a bioinformatics researcher helping labs, PhD students, and biotech teams turn RNA-seq data into reliable biomarker shortlists, with the biology behind them. Many analyses stop at a gene list. I go further and show which candidates are robust, how they connect, and what biology could explain them. What I do ✔ Bulk and single-cell RNA-seq analysis: QC, normalization, differential expression, clustering, annotation ✔ GEO dataset reanalysis and multi-cohort biomarker discovery ✔ Systems biology: co-expression networks (WGCNA), PPI and hub-gene analysis, pathway and regulatory analysis ✔ Mechanistic interpretation of results ✔ ML biomarker panels with rigorous validation (cross-validation, independent cohorts, SHAP) ✔ Publication-quality figures, methods text, and a clear report ✔ Also available: WGS analysis and computer-aided drug design (docking, ADMET, QSAR, MD) How I work You send the dataset, your question, and your deadline. I reply with a short plan, timeline, and fixed price. You receive reproducible R/Python code, figures, and a report. Deliverables describe what the data support, with every method documented. Interpretation, claims, and publication decisions remain yours. Tools: DESeq2, edgeR, limma, Seurat, Scanpy, WGCNA, STRING/Cytoscape, clusterProfiler, GSEA, scikit-learn, XGBoost, SHAP, AutoDock Vina, GROMACS, RDKit