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.

icon
  • Bayesian networks analysis of malocclusion data

    MSPAGCG. CaldarelliLF Scientific Reports

    Bayesian analysis of medical data

    Bayesian networks describe the evolution of orthodontic features on patients receiving treatment versus no treatment for malocclusion.

  • Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction detection experiments

    AAACA. CoolenNP Journal of the Royal Society Interface

    Protein interaction experiments

    Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.

  • Using networks to understand medical data: the case of class III malocclusions

    ASA. ScalaPAMSGCG. CaldarelliJMLF PLoS ONE

    Networks for medical data

    Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.

  • What you see is not what you get: how sampling affects macroscopic features of biological networks

    AAACA. Coolen Interface Focus

    What you see is not what you get

    Methods from tailored random graph theory reveal the relation between true biological networks and the often-biased samples taken from them.