Katharina von Loga

This study uses deep learning to predict YAP1/TEAD activity from histology slides, linking histological patterns to disease aggressiveness in cancer patients.

The paper compares three transcriptomic methods on FFPE tumor samples to assess their ability to detect inter- and intra-patient heterogeneity.

This paper develops an error-corrected cfDNA sequencing method for highly sensitive mutation detection and genome-wide copy number reconstruction in metastatic colorectal cancer.