Integrating literature-constrained and data-driven inference of signalling networks.

ABSTRACT:

Motivation

Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas 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 informatio

SUBMITTER: Eduati F 

PROVIDER: S-EPMC3436796 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature