Shannon Bell leads a group of modelers, informaticians, and toxicologists housed within the Bioinformatics and Computational Biology Program who use their understanding of toxicology, ecology, chemistry, and data science to further our understanding of how chemicals interact with biological systems. She uses her expertise in data analysis and integration to support knowledge discovery and decision making. She has used modeling and machine learning techniques to address research questions in different biological species including Arabidopsis, cereal crops, rodents, and humans.
Shannon’s current research focuses on how we can integrate data streams to gain insight on how chemicals interact with biology and predict the system-level responses. This involves developing strategies to increase data accessibility, tool/approach development and assessment to provide robust and bespoke solutions needed to navigate the evolving chemical assessment landscape. She works with discipline-specific subject matter experts to help enrich their data using knowledge organization systems and creative search and inference strategies. Applications include development of GUI tools for stakeholder engagement, predictive biology, product development, and risk prioritization.
