1. Predictor discovery in training set 6 Training set SJIA (24 F, 14 Q) POLY (15 F, 10 Q) 1 DIGE raw gel images SJIA (13 F, 13 Q) POLY (5 F, 5 Q) Spot finding spot alignment feature extraction 889 discrete spot features Classifier training Ten-fold cross-validation Classify SJIA F vs Q POLY F vs Q DIGE analysis Prediction analysis of microarray (PAM) Predictor test in testing set 7 Testing set SJIA (24 F, 14 Q) POLY (15 F, 11 Q) Predictors of 4 ~ 12 features PAM class prediction algorithm LDA Analysis of goodness of class separation 2 Cluster analysis 7 feature biomarker panel 2d hierarchical clustering heatmap plotting Normalization manual review Manual review MSMS ID 97 spots P < 10 -5 3 Mann Whitney 10 protein candidates 4 Literature review + literature candidates - check antibody availability 5 12 ELISA assays 8 Prospective Study N.D. Discriminate SJIA F KD FI Figure 1

2. Figure 2 FQFQ SJIAPOLY A2M CFHR1 ATIII GSN A2M GSN TTR APOA1 SAP APOIV HP CRP HP MRP8 MRP14 SAA SAP APOAIV HP CRP HP A2M CFHR1 GSN A2M ATIII TTR APOA1 APOIV APOA1 MRP14 HP MRP8 SAA FQFQ SJIAPOLY FQ SJIA FQ A BCD

3. Goodness of class separation –  probability POLY TrainingPOLY Testing 128754 Feature# 128754 SJIA TrainingSJIA Testing 12765489 765489 Figure 3 A BCD

4. Figure 4 SJIA Q SJIA F SJIA Q SJIA F TRAININGTESTING POLY Q POLY F POLY Q POLY F TRAININGTESTING A BCD Estimated probability Samples

5. Figure 5 Training set n = 38 2414 Clinical diagnosis FQ n = LDA 185 69 Classified as F Classified as Q Percent Agreement with clinical diagnosis 75%64.3% +- 71% Overall P = 3.6 X 10 -2 Testing set n = 38 2414 Clinical diagnosis FQ n = LDA 214 310 Classified as F Classified as Q Percent Agreement with clinical diagnosis 87.5%71.4% +- 81.5% Overall P = 3.9 X 10 -4 B CA Biomarker panel of 7 members 1.HP 2.APO AI 3.A2M 4.SAP 5.CRP 6.MRP8/MRP14 7.SAA SJIA All data n = 76 4828 Clinical diagnosis FQ n = LDA 439 519 Classified as F Classified as Q Percent Agreement with clinical diagnosis 89.6%67.9% +- 81.6% Overall P = 3.3 X 10 -7 D SJIA

6. SAP SAA MRP8 HP CRP A2M MRP14 APOA1 121312 SJIA F KDFI Data set n = 37 13 Clinical diagnosis SJIA F NOT-SJIA F n = Unsupervised clustering 10 3 1 23 Clustered as SJIA F Clustered as NOT-SJIA F Percent Agreement with clinical diagnosis 77%96% + - 85% Overall P = 8.12 X 10 -6 24 Figure 6 SJIA FFIKD 104830 SJIA F KDFI Data set n = 88 48 Clinical diagnosis SJIA F NOT-SJIA F n = PAM 48 0 3 37 Classify as SJIA F Classify as NOT-SJIA F Percent Agreement with clinical diagnosis 100%92.5% + - 96.5% Overall P = 2.2 X 10 -16 40 A BCD