Browsing by Author "Palacios, Julia A."

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  • Cappello, Lorenzo; Kim, Jaehee; Palacios, Julia A. (Public Library of Science (PLoS), 2023)
    The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, ...
  • Cappello, Lorenzo; Palacios, Julia A. (Taylor & Francis, 2022)
    Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model ...
  • Cappello, Lorenzo; Palacios, Julia A. (Institute of Mathematical Statistics, 2020)
    Statistical inference of evolutionary parameters from molecular sequence data relies on coalescent models to account for the shared genealogical ancestry of the samples. However, inferential algorithms do not scale to ...
  • Cappello, Lorenzo; Kim, Jaehee; Liu, Sifan; Palacios, Julia A. (Institute of Mathematical Statistics, 2022)
    Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled ...

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