Investigating the GRNN Oracle as a Method for Combining Multiple Predictive Models of Colon Cancer Recurrence from Gene Microarrays

In previous work, we applied an advanced genetic algorithm method for feature subset selection combined with noise perturbation in an attempt to overcome the over-fitting that is typical with microarray datasets. The method was applied to a dataset from Moffitt Cancer Center and the clinical outcome to be predicted was cancer recurrence in less than 5 years. By its nature, the method yields multiple gene signatures, each as small as possible and often these signatures will share one or more genes. The question is how to combine the predictions from multiple predictors. In the previous work, we produced an ensemble prediction by a simple majority vote rule, and observed that performance on a validation set was considerably worse than on the learning set. Our conclusion was that the training and validation sets were not equally representative of the same population, and therefore could not provide reliable gene signatures. Here we report on an effort to apply a more sophisticated ensemble method, the Generalized Regression Neural network (GRNN) Oracle, but this did not allow us to reverse our original conclusion.