Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.

ABSTRACT: Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data f

SUBMITTER: Chou RT 

PROVIDER: S-EPMC11561111 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature