Abstract

New technologies are permitting large-scale quantitative studies of signal-transduction networks. Such data are hard to understand completely by inspection and intuition. 'Data-driven models' help users to analyse large data sets by simplifying the measurements themselves. Data-driven modelling approaches such as clustering, principal components analysis and partial least squares can derive biological insights from large-scale experiments. These models are emerging as standard tools for systems-level research in signalling networks.