Cancer Biomarker Discovery Platform
Research-grade bioinformatics pipeline — integrating genomics, transcriptomics, and proteomics data to identify candidate cancer biomarkers with reproducible, validated methodology.
Researcher & Engineer
An end-to-end computational platform for pharmaceutical-grade cancer biomarker discovery. Integrates multi-omics data, machine learning, and clinical validation frameworks for precision oncology applications.
Problem
Cancer biomarker research is data-rich but tool-poor. Researchers spend months on:
- Assembling data from unrelated sources (genomics, transcriptomics, proteomics)
- Normalizing across wildly different scales and formats
- Reimplementing the same analysis pipelines
- Publishing results without reproducibility guarantees
The bottleneck isn’t the biology — it’s the engineering.
What I Built
A reproducible computational research platform:
- Multi-source data integration — ingest and normalize genomic, transcriptomic, and proteomic data
- Feature engineering pipelines — dimensionality reduction, batch correction, quality control
- Machine learning — ensemble models for biomarker candidate ranking
- Clinical validation frameworks — statistical rigor and reproducibility checks
- Interactive analysis — Jupyter notebooks for exploration and hypothesis testing
- Reproducible builds — every result is a deterministic function of inputs and code
Stack
- Language: Python
- Data: pandas, NumPy, scikit-learn
- Reproducibility: Jupyter, environment pinning
- Testing: automated pipelines with snapshot verification
Result
A pipeline that compresses months of ad-hoc analysis into hours of structured computation — with every step auditable, every parameter logged, every result reproducible.
Related Work
- ARGUS Platform — public health intelligence system with the same multi-source data philosophy applied to epidemiological surveillance.
- Microbiome Analysis Project — computational pipeline for differential microbial abundance analysis.
Lessons
- Biology is a data problem. The questions are biological; the answers are computational.
- Reproducibility is not optional. In research, a pipeline that can’t be rerun is a pipeline that doesn’t exist.
- Generalize the infrastructure. What I built for cancer biomarkers works for any multi-omics integration problem.
