About Me
I’m an Computational Cancer Biologist/Embedded Bioinformatician (Postdoctoral Researcher) at the Department of Biomedicine (DBM), a joint venture between the University of Basel and University Hospital Basel. With over 8+ years of experience in computational biology and a PhD in drug development and PK/PD modeling, I specialize in leveraging multi-omics data and machine learning to advance precision medicine in oncology and complex diseases.
My work focuses on identifying novel biomarkers in gynecological cancers, particularly rare gynecological cases and ovarian carcinomas, with the goal of enabling personalized treatment strategies and new therapeutic strategies. I combine strong computational expertise with deep biological insight to tackle complex challenges in cancer research.
I am also developing new computational algorithms and tools that can be used by the community (see drugsens).
Current Position
Computational Cancer Biologist (Postdoctoral Researcher)
Department of Biomedicine (DBM), University of Basel & Universitätsspital Basel
2021 - Present
I work at the intersection of computational biology and clinical research, implementing complex data integration for personalized medicine and establishing bioinformatics workflows for single-cell and bulk data analysis from clinical cancer cases.
My role involves:
- Leading multi-omics integration projects for rare gynecological cancers and ovarian carcinoma
- Single cell data integration with bulk technology
- Developing/Implementing scalable bioinformatics pipelines using Nextflow for reproducible research
- Collaborating with clinical teams to translate computational insights into actionable biomarkers
- Develop computational tools and algorithms for transnational research
- Teaching SIB courses on cancer genomic variants analysis
- Mentoring BSc/MSc students in computational biology
- Learning cutting edge technologies, such as third generation sequencing (PacBio and Nanopore)
Key Achievements
- 🧬 Recent Breakthrough:
- Identified ATR-FTIR spectra that, with >90% accuracy, discriminate healthy individual from gynecological cancer patients, using fresh usrine for an extremely rapid, fast and inexpensive measurement (2025) -> Paper
- Identified ATR-FTIR spectra that, with >90% accuracy, discriminate healthy individual from gynecological cancer patients, using fresh usrine for an extremely rapid, fast and inexpensive measurement (2025) -> Paper
- 💊 Drug Discovery: Led comprehensive assessment revealing overlapping dependencies for PARPi and chemotherapy response in ovarian cancer (2024)
- Identified predictive biomarkers for personalized treatment in vulvar squamous cell carcinoma through integrated multi-omic analysis (2023)
- 🛠️ Software Development: Published CRAN packages and open-source integrated-workflow for complete analysis of QuPath derived data for the bioinformatics community. Reducing the data analysis time of over 90% drugsens
- 🏢 Industry Experience: I successfully completed a data science internship at Novartis Basel, developing R-Shiny applications for Phase III clinical trials’ primary endpoints analysis
Research Focus
Core Programming:
R (Expert) Python (Advanced) Bash (Advanced) SQL (Proficient)
Specialized Skills:
Single-cell Analysis Multi-omics Integration Machine Learning Nextflow/Snakemake Pipelines Docker/Singularity LLMs fine tuning
Languages
Effective communication across cultures is essential in Basel’s international biotech environment:
- 🇬🇧 English (C2 - Professional)
- 🇮🇹 Italian (Native)
- 🇩🇪 German (B1)
- 🇵🇹 Portuguese (B2)
- 🇪🇸 Spanish (B1)
Feel free to reach out at fl@flaviolombardo.site or connect on LinkedIn.