We applied proteomics data obtained from 2DE and 2D-DIGE analyses to discriminate among FM, VM, FC and HC groups using multivariate statistical analysis. 5 proteins (haptoglobin, apolipoprotein A-I, hemopexin, apolipoprotein E and serum amyloid A) identified by 2DE (Table S8 .1A) and 7 proteins (haptoglobin, apolipoprotein A-I, hemopexin, apolipoprotein E serum amyloid A, serum amyloid P and serum paraoxonase/ arylesterase 1) identified by 2D-DIGE (Table S8 .2A) were used to develop statistical classifier designed to categorize and predict clinical phenotypes (i.e., FM, VM and HC). Discrimination of malaria (FM and VM) from FC (leptospirosis) and statistical sample class prediction was performed on the basis of differential expression levels of 6 candidate proteins (serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I) (Table S8 .3A). Selection of the candidate proteins was executed on the basis of their level of differential expression and ability to discriminate between the clinical phenotypes. For multivariate statistical analysis and machine learning, the data were mean centered; scaled and logarithmic transformation was performed in order to lower relatively large differences among the respective spot abundances. 2DE data was additionally normalized using Quantile method to correct for batch difference. 3 levels of validation were used to establish the reliability of identified differentially expressed proteins to detect correct phenotypic classes using Mass Profiler Professional (MPP).