Nicolaj S. Hackert

This study profiles B cell activation states using multi-omics data to identify genetic mechanisms underlying immune-mediated diseases.

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.

The paper identifies a non-coding variant that increases ICOS expression, leading to more T peripheral helper cells and contributing to autoimmune diseases.