title: Data-driven modelling of signal-transduction networks.
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.
authors.affiliation: Cell Decision Processes Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Janes
authors.forename: Michael B
authors.initials: MB
authors.lastname: Yaffe
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D008954:Models, Biological; D012044:Regression Analysis; D015398:Signal Transduction; D049490:Systems Biology