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  "Title": "Training of boolean logic models of signalling networks using\nprior knowledge networks and perturbation data",
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  "Date": "2022-03-16",
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  "biocViews": "CellBasedAssays, CellBiology, Proteomics, Pathways, Network,\nTimeCourse, ImmunoOncology",
  "Description": "This package does optimisation of boolean logic networks\nof signalling pathways based on a previous knowledge network\nand a set of data upon perturbation of the nodes in the\nnetwork.",
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  "Date/Publication": "2026-04-28 12:36:01 UTC",
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    "getStimuli",
    "getTimepoints",
    "getVariances",
    "graph2sif",
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    "model2igraph",
    "model2sif",
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    "plotFit",
    "plotModel",
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    "plotOptimResultsPDF",
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    "preprocessing",
    "randomize",
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    "readBND",
    "readBNET",
    "readErrors",
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    "readSBMLQual",
    "readSif",
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    "simulateTN",
    "simulatorT0",
    "simulatorT1",
    "simulatorTN",
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    "writeDot",
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    "writeNetwork",
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    "writeScaffold",
    "writeSIF"
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      "table": false,
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      "fields": [],
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      "tojson": true
    },
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      "fields": [],
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      "table": false,
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      "object": "DreamModel",
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      "table": false,
      "tojson": true
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      "object": "ToyModel",
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      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
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      "title": "Toy model",
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      "fields": [],
      "table": false,
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  "_help": [
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      "page": "CellNOptR-package",
      "title": "R version of CellNOptR, boolean features",
      "topics": [
        "CellNOptR-package",
        "CellNOptR"
      ]
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      "page": "build_sif_table_from_rule",
      "title": "Build a SIF table from a logic rule written in a string",
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      "page": "checkSignals",
      "title": "Check the CNOlist and model matching",
      "topics": [
        "checkSignals"
      ]
    },
    {
      "page": "CNOdata",
      "title": "Get data from a CellNOpt data repository",
      "topics": [
        "CNOdata",
        "cnodata"
      ]
    },
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      "page": "CNOlist-class",
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        "CNOlist-class",
        "compatCNOlist,CNOlist-method",
        "getCues,CNOlist-method",
        "getInhibitors,CNOlist-method",
        "getSignals,CNOlist-method",
        "getStimuli,CNOlist-method",
        "getTimepoints,CNOlist-method",
        "getVariances,CNOlist-method",
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        "plot,CNOlist,CNOlist-method",
        "randomize,CNOlist-method",
        "readErrors,CNOlist-method",
        "setSignals<-,CNOlist-method",
        "writeErrors,CNOlist-method"
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        "compatCNOlist,CNOlist",
        "getCues",
        "getCues,CNOlist",
        "getInhibitors",
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        "length,CNOlist,ANY-method",
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      "topics": [
        "CNOlistDREAM"
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    {
      "page": "CNOlistToy",
      "title": "Toy data",
      "topics": [
        "CNOlistToy"
      ]
    },
    {
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      "title": "Toy data with 2 time points",
      "topics": [
        "CNOlistToy2"
      ]
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    {
      "page": "CNORbool",
      "title": "Simple Boolean analysis standalone",
      "topics": [
        "CNORbool"
      ]
    },
    {
      "page": "CNORwrap",
      "title": "CNOR analysis wrapper",
      "topics": [
        "CNORwrap"
      ]
    },
    {
      "page": "compressModel",
      "title": "Compress a model",
      "topics": [
        "compressModel"
      ]
    },
    {
      "page": "computeScoreT1",
      "title": "Compute the score of a model/data set using a bitString to cut the model.",
      "topics": [
        "computeScoreT1"
      ]
    },
    {
      "page": "computeScoreTN",
      "title": "Compute the score at TN of a model/data set using a bitString to cut the model.",
      "topics": [
        "computeScoreTN"
      ]
    },
    {
      "page": "create_binaries",
      "title": "Defining the set of binary variables for the ILP implementation of CellNOptR.",
      "topics": [
        "create_binaries"
      ]
    },
    {
      "page": "createAndRunILP",
      "title": "Creating and running the ILP problem.",
      "topics": [
        "createAndRunILP"
      ]
    },
    {
      "page": "createILPBitstringAll",
      "title": "Reading the optimal solutions as bitstrings.",
      "topics": [
        "createILPBitstringAll"
      ]
    },
    {
      "page": "crossInhibitedData",
      "title": "If an inhibitor is also a measured species, replace the data with NA (when inhibited)",
      "topics": [
        "crossInhibitedData"
      ]
    },
    {
      "page": "crossvalidateBoolean",
      "title": "k-fold crossvalidation for Boolean model.",
      "topics": [
        "crossvalidateBoolean"
      ]
    },
    {
      "page": "cutAndPlot",
      "title": "Interface to cutAndPlotResults functions.",
      "topics": [
        "cutAndPlot"
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    },
    {
      "page": "cutAndPlotResultsT1",
      "title": "Plot the results of an optimisation at t1",
      "topics": [
        "cutAndPlotResultsT1"
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    },
    {
      "page": "cutAndPlotResultsTN",
      "title": "Plot the results of an optimisation at tN",
      "topics": [
        "cutAndPlotResultsTN"
      ]
    },
    {
      "page": "cutCNOlist",
      "title": "Cut a CNOlist structure according to a model",
      "topics": [
        "cutCNOlist"
      ]
    },
    {
      "page": "cutModel",
      "title": "Cut a model structure according to a bitstring",
      "topics": [
        "cutModel"
      ]
    },
    {
      "page": "cutNONC",
      "title": "Cuts the non-observable/non-controllable species from the model",
      "topics": [
        "cutNONC"
      ]
    },
    {
      "page": "cutSimList",
      "title": "Cut a simList structure according to a bitstring",
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        "cutSimList"
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      "title": "Expand the gates of a model",
      "topics": [
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      ]
    },
    {
      "page": "findNONC",
      "title": "Find the indexes of the non-observable and non controllable species",
      "topics": [
        "findNONC"
      ]
    },
    {
      "page": "gaBinaryT1",
      "title": "Genetic algorithm used to optimise a model",
      "topics": [
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      ]
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      "page": "gaBinaryTN",
      "title": "Genetic algorithm for time point N",
      "topics": [
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      "page": "getFit",
      "title": "Compute the score of a model",
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      "page": "graph2sif",
      "title": "Convert graph to SIF",
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      "title": "ILP method used to optimise a model",
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      "title": "ILP method used to optimise a model",
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      "title": "ILP method used to optimise a model",
      "topics": [
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    },
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      "page": "indexFinder",
      "title": "Finds the indices, in the model fields, of the species that are measured/inhibited/stimulated",
      "topics": [
        "indexFinder"
      ]
    },
    {
      "page": "internals",
      "title": "List of CellNOptR internal functions.",
      "topics": [
        "internals"
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    },
    {
      "page": "invokeCPLEX",
      "title": "Solving the ILP problem with CPLEX.",
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        "invokeCPLEX"
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    {
      "page": "DreamModel",
      "title": "Model used for the DREAM3 challenge",
      "topics": [
        "DreamModel"
      ]
    },
    {
      "page": "makeCNOlist",
      "title": "Make a CNOlist structure",
      "topics": [
        "makeCNOlist"
      ]
    },
    {
      "page": "mapBack",
      "title": "Map an optimised model back onto the PKN model.",
      "topics": [
        "mapBack"
      ]
    },
    {
      "page": "model2igraph",
      "title": "Convert a model object to a igraph object",
      "topics": [
        "model2igraph"
      ]
    },
    {
      "page": "model2sif",
      "title": "Convert a model object in sif format",
      "topics": [
        "model2sif"
      ]
    },
    {
      "page": "normaliseCNOlist",
      "title": "Normalisation for boolean modelling.",
      "topics": [
        "normaliseCNOlist"
      ]
    },
    {
      "page": "pknmodel",
      "title": "pknmodel",
      "topics": [
        "pknmodel"
      ]
    },
    {
      "page": "plot-methods",
      "title": "plot a '\"CNOlist\"' object - methods",
      "topics": [
        "plot,CNOlist,ANY-method",
        "plot.CNOlist"
      ]
    },
    {
      "page": "plotCNOlist",
      "title": "Plot the data in a CNOlist",
      "topics": [
        "plotCNOlist"
      ]
    },
    {
      "page": "plotCNOlist2",
      "title": "Another version of plotCNOlist that allows to plot 2 cnolist in the same layout to compare them. This function uses ggplot2 library. It is recommended for small data sets (about 15 species).",
      "topics": [
        "plotCNOlist2"
      ]
    },
    {
      "page": "plotCNOlistLarge",
      "title": "Plot the data in a CNOlist, for lists with many conditions.",
      "topics": [
        "plotCNOlistLarge"
      ]
    },
    {
      "page": "plotCNOlistLargePDF",
      "title": "Plots a CNOlist into a pdf file, for lists with many conditions.",
      "topics": [
        "plotCNOlistLargePDF"
      ]
    },
    {
      "page": "plotCNOlistPDF",
      "title": "Plots a CNOlist into a pdf file.",
      "topics": [
        "plotCNOlistPDF"
      ]
    },
    {
      "page": "plotFit",
      "title": "Plot the evolution of an optimisation",
      "topics": [
        "plotFit"
      ]
    },
    {
      "page": "plotModel",
      "title": "Plot a model",
      "topics": [
        "plotModel"
      ]
    },
    {
      "page": "plotOptimResults",
      "title": "Plot the data and simulated values",
      "topics": [
        "plotOptimResults"
      ]
    },
    {
      "page": "plotOptimResultsPan",
      "title": "Plots the data and simulated values from any CellNOptR formalism",
      "topics": [
        "plotOptimResultsPan"
      ]
    },
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      "topics": [
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      ]
    },
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      "page": "prep4sim",
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      "topics": [
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        "prep4sim"
      ]
    },
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      "page": "preprocessing",
      "title": "Performs the pre-processing steps",
      "topics": [
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      ]
    },
    {
      "page": "randomizeCNOlist",
      "title": "add noise to the data contained in a CNOlist.",
      "topics": [
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      ]
    },
    {
      "page": "readBND",
      "title": "Read network from BND file",
      "topics": [
        "readBND"
      ]
    },
    {
      "page": "readBNET",
      "title": "Read network from BNET file",
      "topics": [
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      ]
    },
    {
      "page": "readMIDAS",
      "title": "Reads in a CSV MIDAS file",
      "topics": [
        "readMIDAS",
        "readMidas"
      ]
    },
    {
      "page": "readSBMLQual",
      "title": "Read a SBMLQual document and returns a SIF object (as returned by readSIG",
      "topics": [
        "readSBMLQual"
      ]
    },
    {
      "page": "readSIF",
      "title": "Read a SIF file and create a model object",
      "topics": [
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        "readSif"
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    },
    {
      "page": "residualError",
      "title": "Compute the residual error for a dataset",
      "topics": [
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      ]
    },
    {
      "page": "sif2graph",
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      "topics": [
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    },
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    },
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      "title": "Simulation of a boolean model",
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    },
    {
      "page": "simulatorT1",
      "title": "Simulation of a boolean model",
      "topics": [
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    },
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      "page": "simulatorTN",
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    },
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      "page": "toSBML",
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      ]
    },
    {
      "page": "ToyModel",
      "title": "Toy model",
      "topics": [
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      ]
    },
    {
      "page": "ToyModel2",
      "title": "Toy model",
      "topics": [
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    },
    {
      "page": "write_bounds",
      "title": "Writing the set of boundaries for each integer variable for the ILP implementation of CellNOptR.",
      "topics": [
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    },
    {
      "page": "write_constraints",
      "title": "Writing the set of constraints for the ILP implementation of CellNOptR.",
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    },
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      "page": "writeDot",
      "title": "Write a model, and attached features, to a dot file",
      "topics": [
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    },
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      "page": "writeFile",
      "title": "Writing the ILP problem.",
      "topics": [
        "writeFile"
      ]
    },
    {
      "page": "writeMIDAS",
      "title": "Write a CNOlist structure into a MIDAS file",
      "topics": [
        "writeMIDAS"
      ]
    },
    {
      "page": "writeNetwork",
      "title": "Write a previous knowledge network model to a sif file (with attribute files), as well as a dot file",
      "topics": [
        "writeNetwork"
      ]
    },
    {
      "page": "writeObjectiveFunction",
      "title": "Writing the objective function for the ILP implementation of CellNOptR.",
      "topics": [
        "writeObjectiveFunction"
      ]
    },
    {
      "page": "writeReport",
      "title": "Write a report of a CellNOptR analysis",
      "topics": [
        "writeReport"
      ]
    },
    {
      "page": "writeScaffold",
      "title": "Writes the scaffold network to a sif file (with attributes) and to a dot file",
      "topics": [
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      ]
    },
    {
      "page": "writeSIF",
      "title": "Convert a model into a SIF format and save the result in a file.",
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  ],
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  "_vignettes": [
    {
      "source": "CellNOptR-vignette.Rmd",
      "filename": "CellNOptR-vignette.html",
      "title": "Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR",
      "author": "Camille Terfve, Thomas Cokelaer, Aidan MacNamara, Enio Gjerga, Attila Gabor, Panuwat Trairatphisan, Julio Saez-Rodriguez",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Installation",
        "Introduction",
        "Quick Start",
        "Loading the data and prior knowledge network.",
        "Preprocessing the model",
        "Finding and cutting the non observable and non controllable species",
        "Compressing the model",
        "Expanding the gates",
        "Preprocessing function",
        "Training of the model",
        "Plotting the optimised model",
        "Writing your results",
        "The one step version",
        "A real example",
        "A toy example with two time points",
        "A toy example with the ILP implementation",
        "k-fold Crossvalidation",
        "What else"
      ],
      "created": "2022-03-16 09:29:16",
      "modified": "2022-03-22 11:48:44",
      "commits": 3
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  "_indexed": true,
  "_nocasepkg": "cellnoptr",
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