Gottfrid Sjödahl

This study identifies CD44v6 as a driver of bladder cancer aggressiveness and chemoresistance, linking its expression to poor patient outcomes and therapeutic resistance.

This paper evaluates gene expression predictors of lymph node metastasis in bladder cancer using two large cohorts, finding that existing and newly trained models perform poorly in independent validation.

The paper develops and evaluates a classifier for urothelial carcinoma subtypes that is robust across different platforms and preprocessing methods.

The paper develops a machine learning model using transcriptomic data to predict chemotherapy response in muscle-invasive bladder cancer with high accuracy.