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5 Research products, page 1 of 1

  • Research software
  • 2018-2022
  • Open Source
  • Software
  • DOE CODE
  • COVID-19

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  • Open Source
    Authors: 
    Higa, Kenneth; Ushizima, Daniela;
    Publisher: DOE CODE

    From a single data description file, this package generates a simple but complete RESTful web interface to a relational database, in the form of containers that run in a Docker environment. This initial version produces containers that are intended for use on the NERSC ...

  • Open Source English
    Authors: 
    Sandholtz, Sarah H; Drocco, Jeffrey A;
    Publisher: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

    The TargetID pipeline enables rapid identification and characterization of binding sites in SARS-CoV-2 proteins as well as the core chemical components with which these sites interact.

  • Research software . 2021
    Open Source Python
    Authors: 
    Safta, Cosmin; Ray, Jaideep; Blonigan, Patrick; Chowdhary, Kenny;
    Publisher: DOE CODE

    SAND2021-0565 O PRIME is a modeling framework designed for the real-time characterization and forecasting of partially observed epidemics. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. Characterization is the estima...

  • Research software . 2020
    Open Source English
    Authors: 
    Bauer, Travis;
    Publisher: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

    This is software lets one explore the data released as part of the COVID-19 Open Research Dataset Challenge. It downloads and analyzes the natural language text of the data set and then creates a 2D visualization that can be used to explore it. SAND2020-12185 M Sandia N...

  • Open Source
    Authors: 
    Cadena Pico, Jose; Soper, Braden; Ray, Pryadip; Mguyen, Chanh; Chan, Ryan;
    Publisher: DOE CODE

    Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML...