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ROC curve analyses quantify the effectiveness of the experiment selection algorithm.
ROC curve analysis for evaluating the effectiveness of the selected experiments at improving the correlation between genes in the same functional category (further examples for both the GO and MIPS functional categorizations are presented in Supplementary Information S6). (A) Average ROC curves from 10-fold cross validation obtained using all experiments (red) and the experiments selected by our algorithm (green) for four different GO functional categories, two from Arabidopsis and two from yeast. (B) Averaged (1-AUC) scores from ten-fold cross validations – the lower the value of (1-AUC), the better the performance of the classifier. (C) Average (1-AUC) and average ROC curves for two MIPS FunCat terms for Arabidopsis. p values for the t-test between the ten (1-AUC) values from the ten-fold cross-validation obtained using the selected experiments and those obtained using all experiments are reported in blue for both (B) and (C).