GBAsum implements a Guilt By Association (GBA) method based on the sum of incident edge weights

1 | ```
GBAsum(W, ind.positives)
``` |

`W` |
numeric matrix representing the adjacency matrix of the graph |

`ind.positives` |
indices of the "core" positive examples of the graph. They represent the indices of W corresponding to the positive examples. |

Function that implements a Guilt By Association (GBA) method for label ranking based on the sum of edge weights connecting a node to its positive neighbours.

a list with one element:

`p` |
score associated to each node |

Oliver, S., Guilt-by-association goes global, Nature, 403, pp. 601-603, 2000.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Application of GBAsum to the prediction of the DrugBank category Penicillins
# using the Tanimoto chemical structure similarity network
# between 1253 DrugBank drugs
library(bionetdata);
data(DD.chem.data);
data(DrugBank.Cat);
labels <- DrugBank.Cat[,"Penicillins"];
ind.pos <- which(labels==1);
GBAsum(DD.chem.data, ind.pos);
# Application of GBAsum to the prediction of the DrugBank category "Anti_HIV_Agents"
labels <- DrugBank.Cat[,"Anti_HIV_Agents"];
ind.pos <- which(labels==1);
GBAsum(DD.chem.data, ind.pos);
``` |

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