Wilko Weichert

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

The paper constructs a single-cell multiomics atlas of the human pancreas across development, health, and T2D, identifying plastic cell states and lineage trajectories.

The paper profiles proteomes from 1,220 FFPE tumors across six cancers, identifies a new normalization method, and reports a comprehensive pan-cancer proteome resource.

This study evaluates three PDAC molecular signatures across eight datasets, finding inconsistent subtyping and low classification accuracy, suggesting technical biases and the need for multi-omic approaches.

The paper uses single nucleus and in situ RNA sequencing to create a comprehensive atlas of human pancreas cells, revealing cell topographies and differences between adult and neonatal pancreata.

The paper develops a machine learning model using radiomic features from MRI to predict survival and tumor subtype in pancreatic ductal adenocarcinoma with high accuracy.

This paper develops a machine learning model to predict molecular subtypes of PDAC from radiomic features, showing differential chemotherapy response based on subtype.