Biologically structured deep learning for disease mechanism discovery from multi-omics and microbiome data 

DREAM: Differentiable Realism From AI And Modeling 

Taking medical genetics studies of the Greenlandic population to a new level – with focus on cardiometabolic phenotypes (ELEVATE)

Proteins in CONTEXT 

CHEeTAh: CHallenges of Evaluating Teams and Algorithms

(LM)2-SEC: Linguistically Motivated Language Model Security

Charting territories: Refining case-cohort estimation and utilize register-based factors in genetic analyses

Unravelling Immunosenescence from Single Cell and Spatial data

DEVELOP: Using data science to estimate overdiagnosis and mortality reduction in cancer screening programs

GolgiNet: Data science to take glycomics in silico and beyond