Advanced search in Research outcomes
Loading
- software . 2021Open SourceAuthors:Higa, Kenneth; Ushizima, Daniela;Persistent IdentifiersPublisher: 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 ...
Add to ORCID Please grant OpenAIRE to access and update your ORCID works.This research outcome is the result of merged research outcomes in OpenAIRE.
You have already added works in your ORCID record related to the merged research outcome. - software . 2022Open SourceAuthors:Sandholtz, Sarah H; Drocco, Jeffrey A;Publisher: DOE CODE
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.
- software . 2021Open Source PythonAuthors: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...
- software . 2020Open Source EnglishAuthors:Bauer, Travis;Persistent IdentifiersPublisher: 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...
Add to ORCID Please grant OpenAIRE to access and update your ORCID works.This research outcome is the result of merged research outcomes in OpenAIRE.
You have already added works in your ORCID record related to the merged research outcome. - software . 2021Open SourceAuthors:Cadena Pico, Jose; Soper, Braden; Ray, Pryadip; Mguyen, Chanh; Chan, Ryan;Persistent IdentifiersPublisher: 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...
Add to ORCID Please grant OpenAIRE to access and update your ORCID works.This research outcome is the result of merged research outcomes in OpenAIRE.
You have already added works in your ORCID record related to the merged research outcome.