An automated explainable bioinformatics and AI workflow for multi-omic, climate and environmental data, applied to sustainability problems e.g., nature-based carbon capture.
Developing AI and analytics to understand the drivers of study or clinical trial efficiency.
Understanding spatiotemporal heterogeneity across different scales of biological organization.
Modeling the 4D genome with deep learning and stochastic simulations.
Analyzing and visualizing differences in blood flow patterns, made visible with fluorescent dyes and multispectral imaging.
Combining spatial single-cell omics with AI to model the complexity of the tumor micorenvironment and enable novel spatial biomarker discovery.
Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.
An automated explainable bioinformatics and AI workflow for multi-omic, clinical and experimental data, applied to healthcare and drug discovery problems.
An automated explainable bioinformatics and deep learning workflow to elucidate microbiome-metabolite relationships during a host-pathogen interaction.
Clinical language understanding and extraction (CLUE) and electronic medical/health/patient record analytics (EMRA)
Creating cognitive insights from patient records at the point of care.
A novel computational method to quantify cell cycles and cell volume variability