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Motivation: Machine learning methods can be used to support scientificdiscovery in healthcare-related research fields. However, these methods canonly be reliably used if they can be trained on high-quality and curateddatasets. Currently, no such dataset for the exploration of Plasmodiumfalciparum protein antigen candidates exists. The parasite Plasmodiumfalciparum causes the infectious disease malaria. Thus, identifying potentialantigens is of utmost importance for the development of antimalarial drugs andvaccines. Since exploring antigen candidates experimentally is an expensive andtime-consuming process, applying machine learning methods to support thisprocess has the potential to accelerate the development of drugs and vaccines,which are needed for fighting and controlling malaria. Results: We developedPlasmoFAB, a curated benchmark that can be used to train machine learningmethods for the exploration of Plasmodium falciparum protein antigencandidates. We combined an extensive literature search with domain expertise tocreate high-quality labels for Plasmodium falciparum specific proteins thatdistinguish between antigen candidates and intracellular proteins.Additionally, we used our benchmark to compare different well-known predictionmodels and available protein localization prediction services on the task ofidentifying protein antigen candidates. We show that available general-purposeservices are unable to provide sufficient performance on identifying proteinantigen candidates and are outperformed by our models that were trained on thistailored data. Availability: PlasmoFAB is publicly available on Zenodo with DOI10.5281/zenodo.7433087. Furthermore, all scripts that were used in the creationof PlasmoFAB and the training and evaluation of machine learning models areopen source and publicly available on GitHub here:https://github.com/msmdev/PlasmoFAB.

PlasmoFAB: A Benchmark to Foster Machine Learning for Plasmodium falciparum Protein Antigen Candidate Prediction