CNORfuzzy-package: R version of CNOFuzzy, a Constrained Fuzzy Logic Network...

Description Details Author(s) References See Also Examples

Description

This package does optimisation of constrained Fuzzy logic networks of signalling pathways based on a previous knowledge network and a set of data collected upon perturbation of some of the nodes in the network.

Details

Package: CNOR
Type: Package
Version: 1.4.0
Date: 2013-08-28
License: GPL-2
LazyLoad: yes
Depends: R (>= 2.15.0), CellNOptR (>= 1.3.29), nloptr (>= 0.8.5)

Author(s)

M.K. Morris
Maintainer: T. Cokelaer <[email protected]>

References

  1. J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction, Molecular Systems Biology, 5:331, 2009.

  2. Morris MK, Saez-Rodriguez J, Clarke DC, Sorger PK, Lauffenburger DA (2011). Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli. PLoS Comput Biol. 7(3): e1001099.

See Also

CellNOptR package.

Examples

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    # Get data from CellNOptR package
    data(CNOlistToy,package="CellNOptR")
    data(ToyModel,package="CellNOptR")

    # Use the default parameters and set Data and Model
    paramsList=defaultParametersFuzzy()
    paramsList$data<-CNOlistToy
    paramsList$model<-ToyModel

## Not run: 
    # Run the simulator
    Res = CNORwrapFuzzy(data=CNOlistToy, model=ToyModel, paramsList=paramsList)
    
## End(Not run)

CNORfuzzy documentation built on Nov. 1, 2018, 2:23 a.m.