LitCovid


36350613	LitCovid in 2022: an information resource for the COVID-19 literature. [PMID: 36350613] Qingyu Chen, Alexis Allot, Robert Leaman, Chih-Hsuan Wei, Elaheh Aghaarabi, John J Guerrerio, Lilly Xu, Zhiyong Lu Abstract 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 COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature. Nucleic Acids Res. 2023:51(D1) \| 13 Citations (from Europe PMC, 2025-03-22)
35849027	A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles. [PMID: 35849027] Sheng-Jie Lin, Wen-Chao Yeh, Yu-Wen Chiu, Yung-Chun Chang, Min-Huei Hsu, Yi-Shin Chen, Wen-Lian Hsu Abstract In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/. Database (Oxford). 2022:2022() \| 1 Citations (from Europe PMC, 2025-03-22)
33166392	LitCovid: an open database of COVID-19 literature. [PMID: 33166392] Chen Q, Allot A, Lu Z. Abstract Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10,000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others. Nucleic Acids Res. 2021:49(D1) \| 127 Citations (from Europe PMC, 2025-03-22)

LitCovid in 2022: an information resource for the COVID-19 literature. [PMID: 36350613]

Qingyu Chen, Alexis Allot, Robert Leaman, Chih-Hsuan Wei, Elaheh Aghaarabi, John J Guerrerio, Lilly Xu, Zhiyong Lu

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 COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

Nucleic Acids Res. 2023:51(D1) | 13 Citations (from Europe PMC, 2025-03-22)

A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles. [PMID: 35849027]

Sheng-Jie Lin, Wen-Chao Yeh, Yu-Wen Chiu, Yung-Chun Chang, Min-Huei Hsu, Yi-Shin Chen, Wen-Lian Hsu

Abstract

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.

Database (Oxford). 2022:2022() | 1 Citations (from Europe PMC, 2025-03-22)

LitCovid: an open database of COVID-19 literature. [PMID: 33166392]

Chen Q, Allot A, Lu Z.

Abstract

Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10,000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

Nucleic Acids Res. 2021:49(D1) | 127 Citations (from Europe PMC, 2025-03-22)

URL:	https://www.ncbi.nlm.nih.gov/research/coronavirus/
Full name:	A literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus
Description:	LitCovid is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus. It is the most comprehensive resource on the subject, providing a central access to 375,727 (and growing) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics (e.g. Long Covid) and geographic locations for improved access.
Year founded:	2020
Last update:	2023-09-09
Version:
Accessibility:	Accessible
Country/Region:	United States

Data type:	Other
Data object:	Virus
Database category:	Literature
Major species:	Severe acute respiratory syndrome coronavirus 2
Keywords:	SARS-CoV-2 COVID-19

University/Institution:	National Center for Biotechnology Information
Address:	National Institutes of Health (NIH), Bethesda, MD 20892, USA
City:
Province/State:
Country/Region:	United States
Contact name (PI/Team):	Zhiyong Lu
Contact email (PI/Helpdesk):	zhiyong.lu@nih.gov

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