Translational Biomarker Analysis in Oncology
Longitudinal analysis of patient-derived samples to identify response-associated biomarkers. Combined NGS data with clinical endpoints to support go/no-go decisions in an early-phase trial.
PhD → Pharma / Biotech
Welcome
I'm Ryan Lowell, a PhD researcher transitioning into the pharmaceutical and biotech industry. I work at the intersection of molecular biology, data analysis, and translational science.
This portfolio highlights projects, publications, and technical skills that demonstrate how my academic work translates into impact for drug discovery, clinical development, and data-driven decision making.
Research & Projects
Selected work reframed for industry relevance. Replace these examples with your own projects.
High-Throughput Screening Data Pipeline
Built a reproducible analysis workflow for plate-based screening data, including QC, normalization, and hit selection, making results easier to review for non-technical stakeholders.
Real-World Data Exploratory Study
Exploratory analysis of anonymized EHR data to characterize treatment patterns and outcomes in a specific patient subgroup, informing future clinical strategy.
PhD / Researcher Portfolio Website
A modern academic portfolio template designed for researchers. It presents research projects, publications, technical skills, and a downloadable CV in a clear and professional structure. This project addresses a common gap in academia: most researchers rely on static PDF resumes, while a dynamic online portfolio communicates scientific work more effectively.
Publications & Presentations
Example entries — you can swap these for real DOIs, conference talks, or posters.
- Lowell R, et al. "Molecular signatures of treatment response in solid tumors." Journal of Translational Oncology, 2024.
- Lowell R, et al. "From bench to bedside: aligning academic biomarkers with clinical endpoints." Talk at BioPharma Data Summit, 2023.
- Lowell R, et al. "Scalable pipelines for pre-clinical assay data." Poster at Computational Life Sciences Conference, 2022.
Technical Skills
Focused on what matters to pharma / biotech teams: data, biology, and collaboration.
Biology & Assays
- Molecular & cell biology techniques
- NGS & gene expression profiling
- Biomarker discovery & validation
- In vitro models & functional readouts
Data & Programming
- Python, R
- Pandas, tidyverse, basic ML workflows
- Reproducible analysis (Git, notebooks)
- Data visualization for decision makers
Collaboration & Communication
- Cross-functional work with clinicians & statisticians
- Scientific communication for non-experts
- Stakeholder-oriented reporting & slide decks
- Mentoring junior researchers
Contact
Simple contact section — you can later swap this for a form handler.