Van Wyk, Roelof Daniel JacobusVan Biljon, RietteBirkholtz, Lyn-Marie2022-05-172022-05-172021-07-14Van Wyk, R., Van Biljon, R., Birkholtz, L.-M. 2021, 'MALBoost : a web‑based application for gene regulatory network analysis in Plasmodium falciparum', Malaria Journal, vol. 20, art. 317, pp. 1-9.1995-592810.1186/s12936-021-03848-2https://repository.up.ac.za/handle/2263/85241Additional file 1. AP2-G GRN network composition and validation.BACKGROUND : Gene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers (http:// malbo ost. bi. up. ac. za). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets. METHODS : The web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users. RESULTS : A web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIPseq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets. CONCLUSION : The MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving “traditionally” low-level signatures even in bulk RNA datasets.en© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.MalariaPlasmodium falciparumMachine learningGene regulatory network (GRN)Artificial intelligence (AI)Article