Vaccine development is an empirical process (trial and error) and involves a long, expensive clinical development pipeline to license an efficacious vaccine candidate. Better tools for vaccine evaluation are needed to adapt to a rising number of candidate vaccines entering clinical trials for many diseases. Surrogate biomarkers of immunity offer the possibility of expediting the clinical development by eliminating non-viable candidates earlier in the pipeline, shortening vaccine trial timeframes by giving a proxy measurement for efficacy and by guiding future vaccine design. In the case of malaria and other complex diseases, a surrogate biomarker of immunity has been difficult to achieve with classical immunological assays. We propose using a systems biology analytical approach in two efficacious malaria vaccination models to identify combinatorial biomarkers of protection. First, newly generated cellular transcriptome profiles and previously generated immunological read-outs common to both trials will be integrated into a database for this analysis. An already developed artificial intelligence-based analytical tool that generates biological network maps, transforms experimental data to the map and discriminates transcriptional gene signatures to physiological states (protection or susceptibility) will be applied in both vaccination models. The aim is to determine malaria signatures of protection that will then be refined and validated in an experimentally induced immunity non-human primate model. The optimized model will be further validated on additional samples from the two protective human trials. The identified biomarkers of protection will be used to produce a customised Immunome Chip, which together with traditional immunological read-outs will be used to evaluate vaccine efficacy, shortening times and costs of clinical trials. This strategy may also prove useful for other diseases and support the systems medicine approach.