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

Abstract

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. Multivariate Adaptive Regression Splines models allow to select a set of the most valuables proteins from the database for modelling. These models were trained in 9 different degrees and chosen regarding its performance and complexity. Artificial neural networks models were trained on with data restricted to the most significant variables. The performance of both type of models were analysed in terms of the Area Under Curve (AUC) of the Receiver Operating Characteristics (ROC) curve. The ANN technique resulted in a model with AUC of 0.62006, while for MARS technique, the value was of 0.569312 in the best situation. Then, the artificial neural network model obtained can categorize if a patient suffer prostate cancer significantly better than MARS models and with high rate of success.