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irem
@irem_es
Bioinformatics Developer and Genetics and Bioengineering
Türkei
Englisch, Deutsch, Türkisch
Über mich
As a Genetics and Bioengineering graduate, I bridge the gap between wet-lab bioprocesses and dry-lab computational pipelines. I build production-grade, modular Python workflows to clean, analyze, and visualize complex genomic data (NGS, RNA-Seq, and sequence analysis).
I specialize in transforming raw datasets into clinical-grade, publication-ready reports (Volcano plots, Heatmaps) for biotech startups and research labs.
Let's build reproducible, turnkey pipelines to accelerate your research.
Kompetenzen
irem
offline •
Durchschnittliche Antwortzeit: 1 Stunde
Meine Dienstleistungen
Statistische Modellierung & Analyse
I will do advanced rna seq and single cell data analysis
Portfolio
Arbeitserfahrung
Upwork
Freiberufler • 4 mos
Freelance Bioinformatics Developer
Jun 2026 - Jun 2026 • 0 mos
• Processed and formatted Mitochondrial COI sequences to meet strict NCBI standards for official GenBank submissions. • Developed automated Python scripts for sequence curation, quality filtering (QC), and genomic data wrangling.
Freelance Bioinformatics Developer
Apr 2026 - Jun 2026 • 2 mos
Developed a diagnostic pipeline for breast cancer classification using the WBC repository. I engineered a query-optimized data infrastructure from raw clinical matrices to effectively isolate tumor phenotypes. By utilizing Python, Pandas, and Seaborn, I successfully modeled the spatial and volumetric variances between malignant and benign cohorts. This analysis identified "Mean Radius" as a high-weight diagnostic biomarker and established a robust baseline for implementing Logistic Regression and Random Forest predictive models
Freelance Bioinformatics Developer
Apr 2026 - Jun 2026 • 2 mos
Developed an automated bioinformatics pipeline for high-throughput RNA-Seq matrix analysis. I engineered a synthetic dataset to model differential gene expression across patient and control cohorts. Using Python (Pandas/NumPy), I processed a 50-gene expression matrix, applying Gaussian distribution to simulate physiological variability. The workflow concludes with high-resolution Seaborn heatmaps (RdBu scale) that provide publication-ready visualization of up-regulated and down-regulated biomarkers.
