Geert-Jan Huizing

This study evaluates foundation models for predicting cellular responses to perturbations, finding that some pretrained embeddings improve predictions while others do not.

The paper introduces STORIES, a method for learning cell fate landscapes from spatial transcriptomics data, showing improved spatial coherence in neural regeneration and gliogenesis analyses.

Mowgli integrates paired single-cell multi-omics data using a combination of NMF and OT, improving clustering and interpretability, and is benchmarked against state-of-the-art methods.

This paper introduces Optimal Transport as a cell-cell similarity metric for single-cell omics data, showing improved clustering performance across multiple datasets.