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Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches

Abstract : Predicting the outcome of bacterial colonization and infections, based on extensive genomic and transcriptomic data from a given pathogen, would be of substantial help for clinicians in treating and curing patients. In this report, genome-wide association studies and random forest algorithms have defined gene combinations that differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases, while it revealed the importance of IGRs and CDS, but not small RNAs (sRNAs), in anticipating an outcome.
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https://hal.archives-ouvertes.fr/hal-03728161
Contributor : Maryse Collin Connect in order to contact the contributor
Submitted on : Wednesday, November 9, 2022 - 3:41:15 PM
Last modification on : Wednesday, November 16, 2022 - 4:34:29 PM

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Mohamed Sassi, Julie Bronsard, Gaetan Pascreau, Mathieu Emily, Pierre-Yves Donnio, et al.. Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches. mSystems, 2022, pp.article n° : 00378-22. ⟨10.1128/msystems.00378-22⟩. ⟨hal-03728161⟩

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