We are an interdisciplinary group that combines methods from deep learning, image analysis, bioinformatics, and genetics to study the genetic and environmental factors underlying tissue architecture and organisation. Changes in tissue architecture is implicated in many diseases, but how these changes drive disease progression is not well understood. We aim to determine how tissue architecture can impact cell signalling, through mechanosensing, and the interactions between different cell types in the tissue. We believe that these signatures will reveal predictive biomarkers of cancer progression and patient outcomes. To this end, we develop new and bespoke approaches for analysing large-scale biomedical data.