6 citations · 8 across the 4 of their papers we have counts for
7 papers
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…
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…
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
Qingyu Chen, Robert Leaman, Alexis Allot +4
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its…
Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
Lana Yeganova, Rezarta Islamaj, Qingyu Chen +8
Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards u…
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
Qingyu Chen, Kyubum Lee, Shankai Yan +3
Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-pr…
Biomedical Mention Disambiguation using a Deep Learning Approach
Chih-Hsuan Wei, Kyubum Lee, Robert Leaman +1
Automatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous…