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Characterizing Scales of Genetic Recombination and Antibiotic Resistance in Pathogenic Bacteria Using Topological Data Analysis

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Brain Informatics and Health (BIH 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8609))

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Abstract

Pathogenic bacteria present a large disease burden on human health. Control of these pathogens is hampered by rampant lateral gene transfer, whereby pathogenic strains may acquire genes conferring resistance to common antibiotics. Here we introduce tools from topological data analysis to characterize the frequency and scale of lateral gene transfer in bacteria, focusing on a set of pathogens of significant public health relevance. As a case study, we examine the spread of antibiotic resistance in Staphylococcus aureus. Finally, we consider the possible role of the human microbiome as a reservoir for antibiotic resistance genes.

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Author information

Authors and Affiliations

  1. Department of Physics, Columbia University, New York, USA

    Kevin J. Emmett

  2. Department of Systems Biology and Department of Biomedical Informatics, Columbia University, New York, USA

    Raul Rabadan

Authors
  1. Kevin J. Emmett
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  2. Raul Rabadan
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślȩzak

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

    Ah-Hwee Tan

  3. Computational Intelligence Laboratory, ECE Department, University of Manitoba, R3T 5V6, Winnipeg, MB, Canada

    James F. Peters

  4. Institute of Computer Science, University of Rostock, Rostock, Germany

    Lars Schwabe

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© 2014 Springer International Publishing Switzerland

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Emmett, K.J., Rabadan, R. (2014). Characterizing Scales of Genetic Recombination and Antibiotic Resistance in Pathogenic Bacteria Using Topological Data Analysis. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_49

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  • DOI: https://doi.org/10.1007/978-3-319-09891-3_49

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09890-6

  • Online ISBN: 978-3-319-09891-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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