Alzheimer new core signature. Finally, in the 5-protein signature, IL-6 is Table 10. The standard deviation of each test is shown on excluded to provide another comparison and the five proteins this table. now become a proper subset of the 18 original proteins uncovered by Ray et al. Table 4 presents the genes included in each signature, indicating the protein name, Entrez GeneID Overall and official name. (‘‘AD’’+‘‘MCI’’) Test set AD Test set MCI Tables 5, 6, 7 and 8 show the results of the 24 classifiers for all AD Er. NAD Er. AD Er. NAD Er. AD Er. NAD Er. the signatures considered. The classifiers marked with a star have a 18 protein Sig. 3.580 3.022 1.692 2.087 2.430 1.982 random component; therefore the average of ten runs with 10 protein Sig. 3.546 6.127 1.721 3.893 2.214 2.729 different seeds is reported. Finally, Tables 9 and 10 summarize the results. 6 protein Sig. 3.165 4.218 1.419 2.798 2.024 1.625 The results of our 5-protein signature are reported in Table 8. 5 protein Sig. 3.520 3.668 1.433 2.175 2.326 1.906 When considering the ‘‘AD’’ test set, average results (over 24 All the signatures show a very similar behaviour with a small standard deviation. classifiers) are obtained by the 5-protein signature, 96% when doi:10.1371/journal.pone.0003111.t010 predicting AD and 90% when predicting non-demented control. It is also worth mentioning that there are four different biomarkers for Alzheimer’s disease [4–6]. Using an integrative classifiers achieving almost 100% accuracy (i.e. having a bioinformatic approach, described in the next sections, we number of errors smaller or equal to 1) for predicting AD on draw our attention to a smaller signature. The 6-protein the ‘‘AD’’ test set. These results are achieved without losing signature was obtained by the analysis of the protein-relation accuracy when predicting non-demented controls on the same graph and interestingly enough, IL-6 is also included in this dataset. Table 11. Number of errors for each classifier when Table 12. Number of errors for each classifier when considering both test sets together (139 samples). considering the ‘‘AD’’ test set (92 samples). Overall errors ‘‘AD’’ test set Method Method 18 10 6 5 18 10 6 5 Simple Logistic 25 25 18 18 NBTree 9 8 3 3 LMT 25 25 18 18 Simple Logistic 11 9 4 4 Logistic 27 24 21 20 LMT 11 20 4 4 MultiClass Classifier 27 24 21 20 Logistic 13 10 7 6 Bayes Net 27 28 22 21 MultiClass Classifier 13 10 7 6 NBTree 26 23 21 21 PAM 10 11 4 5 Na¨ıve Bayes 23 30 23 19 SMO 8 8 4 4 Na¨ıve Bayes Up. 23 30 23 19 Na¨ıve Bayes 6 14 7 3 ClassViaRegression 28 25 23 24 Na¨ıve Bayes Up. 6 14 7 3 Na¨ıve Bayes Simple 23 30 24 20 Bayes Net 10 9 7 6 Kstar 28 41 33 26 Decorate 8.5 9.6 7.2 5.6 Decorate 23.1 28.3 24.7 21.8 Na¨ıve Bayes Simple 6 14 8 4 SMO 20 23 20 19 Kstar 13 18 14 10 Multilayer Perceptron 21.7 21.8 25.6 21.6 Multilayer Perceptron 7.3 8.1 9.4 5.9 PAM 21 22 20 21 Random Committee 9.9 9.7 8.7 8.2 Random Committee 26.1 26.3 26.6 26.1 ClassViaRegression 9 6 9 8 j48 24 24 24 24 Part 9 5 8 8 Ordinal Class Classifier 24 24 24 24 Random Forest 8.1 7.7 7 8.1 LWL 28 28 29 29 LWL 8 8 9 9 Random Forest 24.3 24.3 25.6 26.2 j48 5 5 7 7 Part 25 30 27 27 Ordinal Class Classifier 5 5 7 7 AdaBoost 23 31 27 31 AdaBoost 7 9 9 12 IB1 21 28 33 30 IB1 5 12 14 10 Ibk 21 28 33 30 Ibk 5 12 14 10 Average 24.342 26.821 24.438 23.196 Average 8.45 10.09 7.72 6.53 Agreement % 82% 81% 82% 83% Agreement % 91% 89% 92% 93% The signature with the best performance on each classifier is highlighted in The signature with the best performance on each classifier is highlighted in bold text. bold text. doi:10.1371/journal.pone.0003111.t011 doi:10.1371/journal.pone.0003111.t012 PLoS ONE | www.plosone.org 9 September 2008 | Volume 3 | Issue 9 | e3111