Philip Stegmaier

The paper introduces GLRP, a method to explain Graph-CNN decisions in breast cancer metastasis prediction, identifying patient-specific molecular subnetworks that align with clinical knowledge.

The paper develops word2vec-based embeddings from PubMed abstracts to capture biological relationships and improves metastatic event prediction using graph convolutional networks.

This study identifies clarithromycin as a potential therapeutic for inflammatory bowel disease through in-silico drug repositioning and experimental validation of its effects on NF-κB signaling.