Rens Janssens

MOBER integrates transcriptomic data from cancer cell lines, xenografts, and clinical tumors using deep learning to remove batch effects and improve clinical translatability.

This study benchmarks deep learning and feature engineering methods for analyzing high-content microscopy images to predict phenotypes and cluster cells without manual annotations.

This paper introduces a fully unsupervised deep learning method to cluster cellular images by Mode-of-Action using only pixel intensity values, achieving high accuracy on a labeled dataset.