Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19

  • Ouyang, Sizhuo (Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University) ;
  • Wang, Yuxing (Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University) ;
  • Zhou, Kaiyin (Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University) ;
  • Xia, Jingbo (Hubei Key Lab of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University)
  • Received : 2021.03.17
  • Accepted : 2021.09.13
  • Published : 2021.09.30
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Abstract

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

Keywords

Acknowledgement

This work is partially funded by the HZAU intramural innovative science funding, grant no. 2662021JC008. We would like to express our gratitude to many instructive discussion among BLAH7 Hackathon (https://blah7.linkedannotation.org/home). Qingyu Chen generously introduced LitCovid and PubTator annotation services. Fabio Rinaldi introduced OGER. Steven Vercruysse kindly offered the knowledge representation template in terms of AGAC mined logic for instructive visualization.

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