Wenyi Wang

Wenyi Wang is a Professor at The University of Texas MD Anderson Cancer Center, where she leads research at the intersection of cancer genomics, biostatistics, and tumor biology. Her work focuses on developing statistical and machine-learning models for cancer prognosis, including gene-signature discovery for papillary thyroid carcinoma and neural network approaches to predict cancer progression. She has advanced spatial transcriptomics methodology, introducing the ATLAS penalty for location-aware smoothing and directed acyclic graph models for ligand-receptor interactions in ovarian cancer. Her recent contributions include a Nature Reviews Cancer guide to transcriptomic deconvolution and Bayesian-informed classification of TP53 germline variants for Li-Fraumeni syndrome. Her research integrates molecular diagnostics with computational innovation to improve precision oncology.