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Fig. 2 | Malaria Journal

Fig. 2

From: MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum

Fig. 2

Internal architecture of MALBoost web-based application. The application runs on a CentOS virtual machine (VM). Python formulates the core coding language of the application, running everything from Flask to task queue servers and GRN model implements. The Redis data broker passes data to the Celery queuing server, which tasks individual workers with executing the model construction. A selection of either GENIE3 or GRNBoost2 is offered, for more on the models refer to [12]. Transcriptome and regulatory list data are passed to the Celery worker environment via the SQLite DB. This data is subsequently deleted upon completion of the GRN construction, the results from the GRN is stored in the DB for a period of 3 days. Once model construction is completed a download link is provided to the researcher. The web front-end is rendered via HTML, CSS and JavaScript

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