Function to execute multiple cross-validation with RANKS for a single class.

1 | ```
multiple.ker.score.cv(RW, ind.pos, m = 5, p = 100, init.seed = 0, fun = KNN.score, ...)
``` |

`RW` |
matrix. Kernel matrix or any valid symmetric matrix |

`ind.pos` |
indices of the positive examples. They are the row indices of RW corresponding to positive examples. |

`m` |
number of folds for each cross-validation |

`p` |
number of repeated cross-validations |

`init.seed` |
initial seed for the random generator (def: 0) |

`fun` |
function. It must be a kernel-based score method (default KNN.score) |

`...` |
optional arguments for the function fun |

It performs multiple cross-validation using RANKS to predict the cross-validated scores. The cross-validation is stratified: the folds are constructed separately for each class, to maintain an equal ratio between classes among folds. It computes the scores by averaging across multiple cross validations.

A list with two components:

`av.scores ` |
a vector with the average scores across multiple cross-validations. Elements of the vector av.scores correspond to the rows of RW |

`pos.scores ` |
a vector with the scores of positive elements collected at each iteration |

`ker.score.cv`

, `multiple.ker.score.thresh.cv`

, `rw.kernel-methods`

, `Kernel functions`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Nodel label ranking for the DrugBank category Penicillins
# on the Tanimoto chemical structure similarity network (1253 drugs)
# using 5 fold cross-validation repeated 10 times
# and eav-score with 1-step random walk kernel
library(bionetdata);
data(DD.chem.data);
data(DrugBank.Cat);
labels <- DrugBank.Cat[,"Penicillins"];
ind.pos <- which(labels==1);
K <- rw.kernel(DD.chem.data);
res <- multiple.ker.score.cv(K, ind.pos, m = 5, p = 10, init.seed = 0, fun = eav.score);
# the same but using the NN-score
res <- multiple.ker.score.cv(K, ind.pos, m = 5, p = 10, init.seed = 0, fun = NN.score);
``` |

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