Marc Teunis
Marc Teunis has a PhD in Medical Biology and is Associate Professor ‘Data Science in Life Sciences & Chemistry’. Marc is the team of a research team that focusses on the application of data science approaches in toxicology. Marc also teaches in data science and AI programs, within and outside the University.
Marc has a wide range of interests. For the past 15 years, Marc has been involved in research to new approach methodologies (NAMs). He conducted research on the development and validation of methods for skin-sensitization, mutagenicity, developmental toxicity, and metabolism. Marc moved his attention in research from the wet lab to in-silico modelling and has become a proficient programmer and machine learning model developer. His current research focusses on the application of Artificial Intelligence in chemical risk assessment; the development and application of Large Language Models for synthesis of evidence and data extraction; and the implementation of open science and reproducible research principles in the toxicological research field.
For the development of the data science competences of the university, Marc is a driving force behind the formation of an organization wide data science group, that is intended to support many research projects in the organization. Furthermore, Marc frequently teaches data science workshops and trainings for postdoctoral researchers within the university and in large research collaborations such as ONTOX. In his capacity as data scientist, Marc regularly provides statistical advice and develops visualizations and interactive data exploration tools.
In his spare time, Marc likes to cook and is a regular guest of the bouldering and climbing gyms around Utrecht. He always takes his climbing shoes along, when visiting another country.
"Literate programming should be in every researchers toolbelt, for the sake of reproducibility of science, and to conquer your own chaos."
"Working on Artificial Intelligence (AI) is interesting and challenging, especially given the current developments in the field. Understanding AI requires knowledge of mathematics, statistics, and data. Helping people to understand this and to apply it in their own context is one of the most challenging and compelling tasks for myself. It helps that our research pursues the higher goal of replacing animal testing in toxicology. This is a goal to which I am willing to contribute with great passion."
Fields of expertise
- Toxicology
- Machine Learning
- Data visualization
Publications
- Development and internal validation of a multivariable prognostic model to predict chronic pain after a new episode of non-specific idiopathic, non-traumatic neck pain in physiotherapy primary care practice
- Report of the First ONTOX Stakeholder Network Meeting: Digging Under the Surface of ONTOX Together With the Stakeholders
- The application of natural language processing for the extraction of mechanistic information in toxicology
