The function uses an heuristic approach to calculate the maximum scoring subnetwork. Based on the given network and scores the positive nodes are in the first step aggregated to meta-nodes between which minimum spanning trees are calculated. In regard to this, shortest paths yield the approximated maximum scoring subnetwork. This function can be used if a CPLEX license is not available to calculate the optimal solution.

1 | ```
runFastHeinz(network, scores)
``` |

`network` |
A graph in |

`scores` |
A named vector, containing the scores for the nodes of the network. All nodes need to be scored in order to run the algorithm. |

A subnetwork in the input network format.

Daniela Beisser

`writeHeinzEdges`

, `writeHeinzNodes`

, `readHeinzTree`

, `readHeinzGraph`

, `runHeinz`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
library(DLBCL)
# load p-values
data(dataLym)
# load graph
data(interactome)
# get induced subnetwork for all genes contained on the chip
interactome <- subNetwork(dataLym$label, interactome)
p.values <- dataLym$t.pval
names(p.values) <- dataLym$label
bum <- fitBumModel(p.values, plot=TRUE)
scores <- scoreNodes(network=interactome, fb=bum, fdr=0.0001)
module <- runFastHeinz(network=interactome, scores=scores)
## Not run: plotModule(module)
``` |

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