The emergence and rapid dissemination of antibiotic resistance worldwide threatens medical progress and calls for innovative approaches for the management of multi-drug resistant infections. Phage therapy might represent such an alternative.
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This re-emerging therapy uses viruses that specifically infect and kill bacteria during their life cycle to reduce/eliminate bacterial load and cure infections. The success of phage therapy however relies on the exact matching between both the target pathogenic bacteria and the therapeutic phage. Therefore, having access to a fully-characterized phage library is necessary to start with phage therapy. An essential second step to conceive personalized phage therapy treatments is the capacity to predict the interactions between the target pathogen and its potential phage. To address this, we aim at developing predictive in silico models of phage-bacteria infection networks, using genomic features from sequenced phages and bacteria, and taking advantage of bioinformatics and machine learning techniques.

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Institut des Technologies de l'Information et de la Communication