66 citations · 102 across the 11 of their papers we have counts for
5 papers · 1 filter
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
Robert Leaman, Rezarta Islamaj, Alexis Allot +3
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of S…
LitCovid in 2022: an information resource for the COVID-19 literature
Qingyu Chen, Alexis Allot, Robert Leaman +5
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on…
LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation
Qingyu Chen, Jingcheng Du, Alexis Allot +1
The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a lite…
tmVar 3.0: an improved variant concept recognition and normalization tool
Chih-Hsuan Wei, Alexis Allot, Kevin Riehle +2
Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large sc…
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Qingyu Chen, Alexis Allot, Robert Leaman +35
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design…