Jan H. Rüschoff

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

This study develops a deep learning model to automate Gleason grading of prostate cancer tissue microarrays, achieving agreement with pathologists and expert-level patient stratification.