A Methodology for the Detection of Relevant Single Nucleotide Polymorphism in Prostate Cancer by Means of Multivariate Adaptive Regression Splines and Backpropagation Artificial Neural Networks

The objective of the present paper is to model the genetic influence in prostate cancer with Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Networks (ANNs) techniques for classification. These models will be able to classify subjects that have cancer according to the values of the selected proteins from the genes selected with the models as most relevant. Subjects are selected as cases and controls from the MCC-Spain database and represent a heterogeneous group.

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