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Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P 

Original publication




Journal article


Cancer Med

Publication Date





1225 - 1234


Bladder cancer, Kohonen self-organizing map, molecular markers, progression, Adult, Aged, Aged, 80 and over, Biomarkers, Tumor, Cluster Analysis, Disease Progression, Female, Gene Expression, Humans, Male, Middle Aged, Models, Biological, Mutation, Neoplasm Recurrence, Local, Neoplasm Staging, Neural Networks, Computer, Risk Factors, Urinary Bladder Neoplasms