Hanns‐Martin Lorenz

This paper uses deep learning to predict tissue outcomes in retinal organoids before they are visually discernible, improving understanding of developmental trajectories.

CytoNormPy provides a fast Python implementation of CytoNorm for removing batch effects in cytometry data, with added clustering and visualization tools.

The paper benchmarks machine learning algorithms for cytometry data analysis, finding decision trees provide high accuracy, speed, and memory efficiency for immune cell phenotype quantification.

This study shows that SLE serum affects intestinal organoids by altering mucus, mitochondrial fitness, and gene expression, linking gut barrier dysfunction to autoimmune disease.