Early and accurate diagnosis is critical for the effective treatment and management of malaria. In recent years, multivariate projection methods are being successfully applied to analyze biological data obtained through genomic, transcriptomic or proteomic approaches to study various human diseases, with implications for diagnostics and clinical management [30, 31] . A sub-set of the proteins identified in our proteomic analysis was used to build statistical sample class prediction models to identify the classifier marker proteins for FM, VM and HC discrimination. Interestingly, two key classifier proteins: serum amyloid A and haptoglobin differentially expressed consistently in all of the malaria patients (FM and VM) compared to the control subjects (HC and FC) and remained statistically significant after FDR (Benjamini-Hochberg) and Bonferroni correction of the p-values obtained in t-test; indicating very strong correlation between the expression levels of these two serum proteins and plasmodial infections. The recognition ability of the prediction models for FM, VM and HC discrimination and cross-validation was almost 100% (Table S8) . We controlled for the statistical false discovery rate using three distinct, iterative validation steps: (i) k-fold crossvalidation algorithm for the original cohort, (ii) application of the marker subset identified in the original cohort to classify newly recruited patients, and (iii) performance of the marker subset validated using three well-known machine learning methods. In our study, biological replicates were investigated, i.e. each proteome profile was representative of a different human subject, and hence the data-sets are characterized by low homogeneity, conferring to the protocol a very high level of variability and complexity. Indeed the extreme heterogeneity or large biological variations including gender, age, genetic factors, dietary considerations, environmental factors and drug treatment affects the detection, validation and establishment of ''gold standard'' serological biomarkers [10, 32] . Nonetheless, the accurate discrimination among the FM, VM and control groups obtained by various prediction models on the basis of differentially expressed candidate proteins testifies to the excellent potential of this analytical approach for the detection and discrimination of VM and FM ( Figure 5 ; Figure S9 ). It should also be noted that uncomplicated FM and VM patients with diverse range of parasitemia; mainly low and moderate parasitemic (,5000 parasites/mL blood), were used for the validation of the prediction models (Table 1) . Even so, the discrimination accuracy of the study is very appreciable indicating the capability of our analytical approach for the detection of very low-level of parasitemia, which is highly promising from a diagnostic point of view. Although, diagnosis of malaria on the basis of microscopic examination of thin or thick smears of peripheral blood is the most commonly used and well-accepted method, but it requires highly trained personnel for smear interpretation, frequently fails to distinguish mixed-species infections or diagnose patients with ''sub-microscopic'' parasitemia below the detectable limit of blood smears, and in many areas of endemicity the operating characteristics of microscopy are poor [33, 34] . In quest of an early and accurate diagnosis of malaria and discrimination of Pf, Pv or mixed infection, establishment of serum protein markers can be an attractive approach apart from clinical symptoms and conventional microscopic examination of blood smears. To this end, some of the classifier candidate proteins identified in this study; such as serum amyloid A, paraoxonase, apolipoprotein A-I and E, haptoglobin, hemopexin, and complement C4 are very important due to their functional relevance in malaria pathogenesis and could further be investigated as potential surrogate protein markers for clinical implications. Various rapid diagnostic tests (RDTs) are in practice for malaria diagnosis, which diagnose the infection on the basis of detection of parasite proteins/antigens e.g. histidine-rich protein II (HRP-II) or lactate dehydrogenase (LDH) [35] , whereas for the first time we have demonstrated the discrimination between FM and VM patients based on protein expression in human host. Malaria RDTs are used regularly in clinics due to the low cost, sensitivity and less detection time. However, analysis of frozen specimens of blood from parasitaemic patients using existing RDTs is bit challenging. Another limiting factor is the shelf-life of RDTs, since most of the existing RDTs deteriorate rapidly on exposure to moisture (humidity) and high temperature. Moreover, significant variations may appear between technicians in both RDT preparation and result interpretation process depending on experience of the performer, manual proficiency and visual perception [36] . To this end, serum protein markers can be potential candidates for development of an alternative sensitive diagnostic approach for malaria. Development of highly sensitive biosensors for the identified surrogate proteins might be attractive from a diagnostic point of view.