Tarik Exner

Deep learning predicts tissue outcomes in retinal organoids

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

CytoNormPy enables a fast and scalable removal of batch effects in cytometry datasets.

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

THE IMMUNE RESPONSE AGAINST CANCER IS MODULATED BY STROMAL CELL FIBRONECTIN

The paper identifies tumor-inhibitory fibroblasts that produce fibronectin, which activates signaling pathways in neighboring cells to enhance immune response against early cancer.

Accurate, fast and memory efficient quantification of immune cell phenotypes in cytometry using machine learning

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.