Figure 1

The training-validation protocol employed to evaluate various gene selection and classification approaches in simplified schematic format. The input is a labeled dataset, D, and the Output is an estimate of the validation performance of algorithm A, denoted by P _A The most important steps in the protocol are the training step (Block labeled 'Train') and the validation step (Block labeled 'Validate'). The training step, in turn, consists of two steps, namely 1) the optimization of the gene selection parameter, ϕ, employing a N _i – fold cross validation loop and 2) training the final classifier glven the optimal setting of the selection parameter. The validation step estimates the performance of the optimal trained classifier ($\left({\omega }_A^{*}\right)$) on the completely independent validation set.