Behind the Paper
The real stories behind the latest research papers, from conception to publication, the highs and the lows
A machine-learning approach to phylogenetic tree inference
The following two fields have barely interacted before: artificial intelligence and molecular evolution. To demonstrate proof of concept, we established a machine-learning-based framework that substantially boosts tree-search algorithms, without compromising accuracy!
A timeframe for human evolution
A precise timeframe for human evolution is fundamental to contextualise key events that occurred during the evolution of our lineage. Our new phylogenetic study dates specific speciation events and provides important insights into body mass and encephalization trends in human evolution.
Behind the paper: Machine Learning for Patient Risk Stratification: Standing on, or looking over, the shoulders of clinicians?
Our recent publication, Machine Learning for Patient Risk Stratification: Standing on, or looking over, the shoulders of clinicians?, in npj Digital Medicine examines the question of whether clinical machine learning models truly extend beyond what clinicians already suspect.
