Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
Bayesian networks describe the evolution of orthodontic features on patients receiving treatment versus no treatment for malocclusion.
Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.
Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.
Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.