Andreas Schuppert

This paper demonstrates swarm learning for training AI models on decentralized histopathology data to predict molecular features in colorectal cancer.

This paper introduces a two-step multi-omics modeling approach to predict drug sensitivity in cancer cell lines, identifying key drivers of drug responsiveness and demonstrating improved performance over one-step methods.

This paper identifies methodological challenges in translational drug response modeling in cancer and highlights the need for more robust and transferable modeling approaches.

The paper systematically analyzes chaperome deregulation across 22 solid cancers using gene expression data and develops a tool to visualize proteostasis alterations.

This study shows that stem cell differentiation involves stochastic transitions through hidden microstates, revealing epigenetic memory and a statistical mechanics framework for cellular identity.