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.

Author affiliations

1. Cell Decision Processes Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA, and Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.
2. Cell Decision Processes Center, Center for Cancer Research and Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Correspondence to: Michael B. Yaffe² Email: myaffe@mit.edu

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