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LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19
Sizhuo Ouyang1, Yuxing Wang1, Kaiyin Zhou1
1Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, 430070 Wuhan, China.
Genomics & Informatics
|October 12, 2021
Summary
Researchers created a large, cross-annotated corpus of COVID-19 abstracts to aid knowledge discovery. This resource, LitCovid-AGAC, facilitates understanding the pathological mechanisms of coronavirus disease 2019.
Area of Science:
- Biomedical Natural Language Processing (BioNLP)
- Computational Biology
- Text Mining
Background:
- The dramatic increase in COVID-19 literature necessitates efficient text curation for knowledge discovery.
- BioNLP plays a crucial role in extracting information on COVID-19 mechanisms.
- Existing annotation systems like PubAnnotation facilitate data integration.
Purpose of the Study:
- To construct a comprehensive, cross-annotated corpus of COVID-19 abstracts for enhanced text mining.
- To integrate diverse annotation resources for a richer dataset.
- To facilitate the discovery of hidden knowledge regarding COVID-19's pathological mechanisms.
Main Methods:
- Merged three distinct annotation resources (PubTator, OGER, AGAC) into a unified dataset.
- Utilized the LitCovid dataset comprising 50,018 COVID-19 abstracts.
- Developed the LitCovid-AGAC corpus with 12 distinct labels (Mutation, Species, Gene, Disease, GO, CHEBI, Var, MPA, CPA, NegReg, PosReg, Reg).
Main Results:
- Successfully created the LitCovid-AGAC corpus, a cross-annotated resource for COVID-19 abstracts.
- The corpus integrates 12 types of annotations from multiple sources, providing rich information.
- The dataset is poised to support large-scale text mining for COVID-19 research.
Conclusions:
- The LitCovid-AGAC corpus represents a valuable resource for the BioNLP community.
- This integrated corpus enables deeper investigation into the pathological mechanisms of COVID-19.
- Facilitates advanced text mining and knowledge discovery from the growing body of COVID-19 research.
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