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Molecular subtyping of bladder cancer using Kohonen self‐organizing maps
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...
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Published in: | Cancer medicine (Malden, MA) MA), 2014-10, Vol.3 (5), p.1225-1234 |
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Main Authors: | , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | 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 |
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ISSN: | 2045-7634 2045-7634 |
DOI: | 10.1002/cam4.217 |