ilpBinaryT2: ILP method used to optimise a model

In CellNOptR: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data

Description

This function is the ilp method to be used to optimise a model by fitting to data for time point 2, that should follow optimisation based on time point 1.

Usage

ilpBinaryT2 <- function(cnolist, 
                        model, 
                        sizeFac = 0.0001, 
                        mipGap = 0, 
                        relGap = 0, 
                        timelimit = 3600, 
                        cplexPath, 
                        method = "quadratic",
                        numSolutions = 100, 
                        limitPop = 500, 
                        poolIntensity = 4, 
                        poolReplace = 2)

Arguments

CNOlist	a CNOlist on which the score is based (based on valueSignals[[2]], i.e. data at time 1)
model	a model structure, as created by readSIF, normally pre-processed but that is not a requirement of this function
sizeFac	the scaling factor for the size term in the objective function, default to 0.0001
mipGap	the absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of this parameter, the linear integer optimization is stopped. Default set to 0
relGap	the relative tolerance on the objective value for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool. Default set to 0
timelimit	the maximum optimisation time in seconds, default set to 3600
cplexPath	the path where the cplex solver is stored. Default set to "~/Documents/cplex"
method	the method of writing the objective function (quadratic/linear). Default set to "quadratic"
numSolutions	the number of solutions to save
limitPop	the number of solutions to be generated. Default set to 500
poolIntensity	the Intensity of solution searching. Default set to 4

Value

This function returns a list with elements:

bitstringILPAll	the list of all optimal bitstrings identified
bScore	the best score for each set of bitstrings
time_cplex_only	the time it took for cplex to solve the problem
total_time	the total time for the pipeline to run (writing problem + solving problem + retrieving solutions)
stringsTolScores	the scores of the above-mentioned strings

Author(s)

E Gjerga, H Koch

References

Alexander Mitsos, Ioannis N. Melas, Paraskeuas Siminelakis, Aikaterini D. Chairakaki, Julio Saez-Rodriguez, and Leonidas G. Alexopoulos. Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data. PLoS Comput Biol. 2009 Dec; 5(12): e1000591.

Examples

# Toy Exampple
data("ToyModel", package="CellNOptR")
data("CNOlistToy", package="CellNOptR")
pknmodel = ToyModel
cnolist = CNOlist(CNOlistToy)
model = preprocessing(data = cnolist, model = pknmodel, compression = TRUE, expansion = TRUE)
plotModel(model = model, CNOlist = cnolist)

# Training to data - ILP
## Not run: 
resILP = ilpBinaryT2(cnolist = cnolist, model = model)

## End(Not run)

CellNOptR documentation built on Nov. 8, 2020, 6:58 p.m.