Speakers
Description
Pre-processing and quality control of high-dimensional serological data from Multiplex Bead Assay machines pose a significant bottleneck to the responsible application of machine learning to global health challenges. Driven by the data demands of the PvSTATEM project, an international initiative aimed at malaria elimination, we developed SerolyzeR, an open-source R package designed to streamline this computational workflow.
The package provides researchers and field scientists with accessible, automated tools for quality control and consistent data normalization via parametric standard curve fitting. By parsing multiple Luminex output formats (including xPONENT, INTELLIFLEX, and BIOPLEX) into a unified dataset, the package eliminates formatting inconsistencies and facilitates seamless cross-experiment comparisons. While initially created to support responsible machine learning in the fight against malaria, SerolyzeR offers a robust, generalizable solution with broad applications in disease surveillance and pathogen research. The source code and detailed documentation for the package are available at https://github.com/mini-pw/SerolyzeR.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | malaria, serology, quality control, calibration |
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| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |