The research aims to identify and prioritize previously unknown vaccine antigen candidates with potentially high efficacy against the most prevalent malaria parasite Plasmodium falciparum. Positive-unlabeled random forest (PURF) was applied to learn from the small set of known Plasmodium falciparum antigens and the other proteins with unknown antigenic properties. The research notebook contains data and code generated in the study “Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum.” The notebook also includes instructions on installing the PURF package, retrieving protein variables and assembling machine learning input from the database, as well as code for experimental analysis and plotting.

Creative Commons License
The notebook is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.