ROC plot for Neural Network cross-validation. The training data was divided into multiple (n = 4 in this figure) non-overlapping sets. Each of the n sets was used to train a different neural network (NN) and tested on the remaining data. A Receiver Operating Curve was generated for each trained network by calculating specificity and sensitivity for different values of the cut-off for the output value to discriminate between regulated and unregulated gene targets. The ideal curve would be collinear with the y-axis for x = 0, and then run parallel to the x-axis as the line y = 1.