1 a. 1 b. IL-3 IL-1 IL-11 EGF MIP-1 TNF- IL-6 MCP-3 PDGF-BB 1 c. Clinical Diagnosis n = Classified as AD Classified as non-AD 1 d. Clinical Diagnosis n = Classified as AD Classified as non-AD Alzheimer Training Set n=80 Simple Logistic AD 41 34.9 6.1 85% NDC 39 5.6 33.4 86% AD 42 42 0 85.4% overall p<0.01 Test Set ‘AD’ n= 92 NDC Simple Logistic 39 3 36 96% p<0.01 OD 11 1 10 G-CSF 100% 92% Figure 3. Classification and prediction of clinical Alzheimer’s diagnosis in subjects with Alzheimer’s disease. (a) An undirected graph, where each node corresponds a different protein belonging to the 10-protein signature we identified; each edge indicates the existence of a direct relation as obtained by searching the PubMed database, (using the Pathway Studio software). (b) Identification of the maximum clique of the graph, uncovering a robust 6-protein signature; each node on the clique has a direct relation with each other. Simple Logistic was used to classify and predict Alzheimer’s (AD) and non-Alzheimer’s class, in the training set (c), the blinded test set ‘AD’ (d). All the results are shown in a confusion matrix, for the training set a 10-fold cross-validation was applied 10 times, in both cases Simple Logistic was used with the default parameters of Weka package. All the p-values were calculated using the Fisher exact test. doi:10.1371/journal.pone.0003111.g003 PLoS ONE | www.plosone.org 11 September 2008 | Volume 3 | Issue 9 | e3111