Method for creating a coherent voting network useful in predicting a likelihood of long-term survival of breast cancer in a breast cancer patient, the method including the steps of: a) organizing predetermined data from a cohort of patients in a master matrix having in its row the list of patients, each having an associated survival or nonsurvival class representing whether the patient had survived or not after a tumor removal, and having in its column the expression value of a panel of genes; b) applying a predetermined statistical test to each gene of the master matrix to evaluate which genes better discriminates the survival or not-survival classes, thus obtaining a first candidate panel of genes; c) discretizing the expression value of each of the genes belonging to said first candidate panel in sub-intervals capable of discriminating the survival or not-survival classes, thus obtaining a quantized master matrix; d) converting the quantized master matrix in a first bipartite graph comprising patient-nodes of both classes, and gene-nodes representatives of the sub-intervals of the expression value of genes of the first candidate panel; e) applying a predetermined algorithm to the bipartite graph to obtain a collection of bipartite communities including both patient-nodes and gene-nodes, the collection of communities providing coverage of the nodes of the bipartite graph; f) applying a further predetermined algorithm to the communities obtained at the end of said step e), restricted to the gene-nodes only, thus obtaining a subset of the genes of the first candidate panel constituting a second candidate panel capable of reproducing the same structure of bipartite communities; g) creating an updated bipartite graph (G') comprising nodes-patients and nodes-gene whose genes belongs to the second candidate panel; h) repeating step e) on the updated bipartite graph (G') and checking if the communities obtained are similar to the ones obtained at step e); i) applying, at each community, a decision function on each patient belonging to it, to determine whether to assign her at the survival or not-survival class, then checking which class the decision function has assigned to each patient, for each community, and assigning a final class to the patient based on a majority rule; j) checking, for each patient belonging to the various communities of step i), whether the class assigned is the same as the class of the master matrix, thus obtaining coherent patients; k) checking whether the percentage of coherent patients in the voting network is greater than a predetermined threshold, thus obtaining a coherent voting network. a) organizing predetermined data from a cohort of patients in a master matrix having in its row the list of patients, each having an associated survival or nonsurvival class representing whether the patient had survived or not after a tumor removal, and having in its column the expression value of a panel of genes; b) applying a predetermined statistical test to each gene of the master matrix to evaluate which genes better discriminates the survival or not-survival classes, thus obtaining a first candidate panel of genes; c) discretizing the expression value of each of the genes belonging to said first candidate panel in sub-intervals capable of discriminating the survival or not-survival classes, thus obtaining a quantized master matrix; d) converting the quantized master matrix in a first bipartite graph comprising patient-nodes of both classes, and gene-nodes representatives of the sub-intervals of the expression value of genes of the first candidate panel; e) applying a predetermined algorithm to the bipartite graph to obtain a collection of bipartite communities including both patient-nodes and gene-nodes, the collection of communities providing coverage of the nodes of the bipartite graph; f) applying a further predetermined algorithm to the communities obtained at the end of said step e), restricted to the gene-nodes only, thus obtaining a subset of the genes of the first candidate panel constituting a second candidate panel capable of reproducing the same structure of bipartite communities; g) creating an updated bipartite graph (G') comprising nodes-patients and nodes-gene whose genes belongs to the second candidate panel; h) repeating step e) on the updated bipartite graph (G') and checking if the communities obtained are similar to the ones obtained at step e); i) applying, at each community, a decision function on each patient belonging to it, to determine whether to assign her at the survival or not-survival class, then checking which class the decision function has assigned to each patient, for each community, and assigning a final class to the patient based on a majority rule; j) checking, for each patient belonging to the various communities of step i), whether the class assigned is the same as the class of the master matrix, thus obtaining coherent patients; k) checking whether the percentage of coherent patients in the voting network is greater than a predetermined threshold, thus obtaining a coherent voting network.