Integrating literature-constrained and data-driven inference of signalling networks
published: Oct. 23, 2012, recorded: September 2012, views: 2522
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Description
Motivation: Recent developments in experimental methods allow generating increasingly larger
signal transduction datasets. Two main approaches can be taken to derive from these data a
mathematical model: to train a network (obtained e.g. from literature) to the data, or to infer the
network from the data alone. Purely data-driven methods scale up poorly and have limited
interpretability, while literature-constrained methods cannot deal with incomplete networks.
Results: We present an efficient approach, implemented in the R package CNORfeeder, to
integrate literature-constrained and data- driven methods to infer signalling networks from
perturbation experiments. Our method extends a given network with links derived from the data
via various inference methods, and uses information on physical interactions of proteins to guide
and validate the integration of links. We apply CNORfeeder to a network of growth and
inflammatory signaling, obtaining a model with superior data fit in the human liver cancer HepG2
and proposes potential missing pathways.
Availability: CNORfeeder is in the process of being submitted to Bioconductor and in the meantime
available at www.ebi.ac.uk/cokelaer/cnofeeder/.
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