Abstract
Antimicrobial resistance poses a serious health problem. Computational and statistical approaches have been developed for bacterial antimicrobial resistance discovery and classification. Next generation sequencing technologies allows us to analyze complex metagenomes and the presence of resistomes in them. In this work we apply Bayesian hierarchical spatial model to estimate the relative risk of antimicrobial resistance related taxa by using the available spatial information of the samples.
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Acknowledgements
This work was supported by project BG05M2OP001-1.001-0004 (UNITE) for the coverage of conference fee and the National Scientific Program Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES) for logistics. In addition, this work was partially supported by the financial funds allocated to the Sofia University St. Kl. Ohridski, grant No. 80-10-116/2020.
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Zhelyazkova, M. et al. (2022). Bayesian Hierarchical Modelling for Antimicrobial Resistance. In: Sotirov, S.S., Pencheva, T., Kacprzyk, J., Atanassov, K.T., Sotirova, E., Staneva, G. (eds) Contemporary Methods in Bioinformatics and Biomedicine and Their Applications. BioInfoMed 2020. Lecture Notes in Networks and Systems, vol 374. Springer, Cham. https://doi.org/10.1007/978-3-030-96638-6_9
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DOI: https://doi.org/10.1007/978-3-030-96638-6_9
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