Further comparative analysis with VM [15] indicates that both Pf and Pv infection lead to alteration of multiple serum proteins involved in diverse essential physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades, lipid transport and metabolism, and blood coagulation ( Figure 4 ). Table S7 and Figure S8 summarize different biological pathways and physiological functions associated with the differentially expressed serum proteins identified in FM and VM using multiple analytical tools. serum proteome of HC and FM patients. FM and HC samples were labeled with Cy3 and Cy5 respectively, while the protein reference pool (internal standard) was labeled with Cy2. (D) Graphical and 3D fluorescence intensity representations of few selected statistically significant (p,0.05) differentially expressed proteins in FM patients identified in biological variation analysis (BVA) using DeCyder 2D software. Graphs showing the decrease/increase in the standardized log abundance of spot intensity in FM compared to the HC cohort of the study (n = 8 Initially, the fidelity of a potential biomarker subset containing 5 proteins identified in 2DE (Table S8 .1A) was evaluated for discrimination of FM and HC. As shown in Figure S9A , the two groups (FM and HC) could be clearly classified by phenotype, thereby providing an additional, unbiased estimate of class prediction. Secondly, we applied the class prediction model based on initial cohort (n = 10) to independently predict (assign) the phenotypic class to either FM or HC group on an independent blind group of 16 subjects (8 newly recruited FM patients and 8 HC). The model provided accurate phenotypic classification; and 100% of the FM (n = 19) and 94.74% of the HC (n = 19) subjects were accurately classified into correct phenotypes (Table  S8 .1C). We achieved 97.37% overall prediction accuracy on independent prediction [HC (n = 19) and FM (n = 19)] using partial least squares discriminant analysis (PLS-DA). For the final validation phase, we compared the performance of the biomarker subset using three well-known machine-learning methods: Decision Trees, Naïve Bayes and support vector machines (SVM). Table S8 .1B summarizes the percentage of samples classified during model training, cross-validation and independent prediction respectively, using the three different classifiers. We achieved, 97.37% overall prediction accuracy with SVM, Decision Trees and Naïve Bayes as well, on blinded prediction using the biomarker dataset for FM and HC (n = 19 each).