Susanne M. Rafelski

The paper introduces Mask Interpreter, a method to improve interpretability of image-to-image translation models, validating biological relevance and identifying artifacts in in silico labeling.

The paper uses ML to analyze morphological dynamics of hiPSC-derived endothelial cells under shear stress, identifying a bifurcation between two stable cell states.

The paper presents a hiPS cell model to study EMTs by comparing 2D and 3D cultures, measuring changes in cell function, markers, organization, and environment.

The paper analyzes how cell shape correlates with proteomic features across multiple cell lines using image-based spatial proteomics data.

This paper introduces a morphology-appropriate representation learning framework for 3D multi-piece intracellular structures, showing improved sub-cluster discovery and phenotypic profiling.

The paper analyzes nuclear growth variations in hiPS cell colonies, finding context-dependent and inherited mechanisms shaping growth trajectories.

The paper develops a hybrid agent-based model to simulate hiPS cell colony dynamics, identifies limitations of the CPM approach, and suggests improvements for accurate simulations.