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Description
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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. 

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Instructions
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To open the research notebook, go to the subfolder main_notebook, and click on the HTML file index.html to open it in a web browser. A PDF version of the notebook main_notebook.pdf is also available. To run the code in the research notebook, open R Markdown (.Rmd) files in RStudio. Data generated from the notebook are stored in the subfolders other_data (structured data), pickle_data (Python objects), and rdata (R objects). 


The notebook is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-nc-sa/4.0/).

Copyright [2023] [Renee Ti Chou and Michael P. Cummings]