| Literature DB >> 35308924 |
Sydney L Foote1,2, Sara Jones1,2, Jane Lockmuller1, Liliana Brown3, Joseph Breen4, Anupama Gururaj4.
Abstract
After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) have become increasingly important to understand the immune response to SARS-CoV-2. The vast amount of preprint and published literature related to COVID-19 makes it challenging for researchers to stay up to date on research results regarding CoPs against SARS-CoV-2. To address this problem, we developed a machine learning classifier to identify papers relevant to CoPs and a customized named entity recognition (NER) model to extract terms of interest, including CoPs, vaccines, assays, and animal models. A user-friendly visualization tool was populated with the extracted and normalized NER results and associated publication information including links to full-text articles and clinical trial information where available. The goal of this pilot project is to provide a basis for developing real-time informatics platforms that can inform researchers with scientific insights from emerging research. ©2021 AMIA - All rights reserved.Entities:
Mesh：
Year: 2022 PMID： 35308924 PMCID： PMC8861695
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076