Eszter N. Tóth
Director of Data Science @Relation
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WORK HISTORY
Director of Data Science @Relation
Data science lead for a Lab-in-the-Loop machine-learning platform, one of several machine learning (ML) technologies underpinning Relation’s target discovery and validation engine- Partnered closely with machine-learning scientists and the wet lab to define data requirements, experimental design, and analytical strategies fit for purpose for ML-driven target discovery and validation, with a strong focus on model performance, interpretability, and biological relevance- Led end-to-end execution: pipeline development and downstream analysis of multi-omics datasets, and transformation of datasets into ML-ready formats- Worked closely with the Target Validation Team to translate ML outputs and experimental results into actionable biological insights supporting target validation and prioritisation- Played a key role in the generation of patient single-cell atlases and structured data packages for therapeutic targets, integrating internal and public datasets to support hypothesis generation- Helped shape best practices for single-cell atlas generation, multi-omics integration, and data standardisation- Mentored and trained junior data scientists, contributing to capability building.
EDUCATION
University of Szeged
Bachelor’s Degree, Chemistry
University of Szeged
Master’s Degree, Chemistry
University of Tsukuba
Doctor of Philosophy (Ph.D.), Human Biology
SKILLS
ABOUT ESZTER N. TÓTH
I am a genomics expert working at the interface of computational biology and functional genomics. My main research interests are single-cell multi-omics and machine learning to discover and validate novel therapeutic candidates. My previous experience includes single-cell immunology, spatial transcriptomics and RNA transport in neuronal cells. I am enthusiastic about multi-disciplinary teamwork and creating a shared vocabulary that allows team members from various backgrounds to work together.
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