George Wright

GOTSurv models patient-specific disease progression from cross-sectional survival data, identifying heterogeneous risk strata and individualized progression trajectories without predefined risk cut-offs.

The paper develops a deep learning tool to analyze endometrial histology and identifies morphological correlates of miscarriage risk.

DCAFA analyzes histological images to identify compositional shifts and feature associations in biomedical data using a regression-based framework.

GOTFlow infers directed population transitions from cross-sectional biomedical data using optimal transport, revealing biologically meaningful dynamics in endometrial remodelling, breast cancer risk, and prion disease.