AGAC on Covid-19 Literature

A page to visualize the result of AGAC annotation on Covid-19 literatures.

The relation visualization of AGAC labels and LitCovid annotated genes. the nodes are the genes in LitCovid and the words that AGAC labeled except the red and blue nodes which represent the PosReg and NegReg labels. Lines between the nodes represent the nodes are at least co-occurrenced in one sentence. The size of the node represents the degree of the node, the bigger the size the larger degree of the node, while the the thickness of the line represent the times that the adjacent nodes occurrence in same sentence.
Click here to see the dynamic visualization of annotation result.
Dynamic Visualization

The amount of Covid-19 related literatures is increasing rapidly, and there are a lot of descriptions about the activities of the virus in organisms. Interested in the changed biological processes in these literatures, AGAC corpus was used to annotate the texts in LitCoivd.

Data

AGAC corpus

LitCovid

Annotation

Since AGAC is concentrated on the biological processes caused by mutation, some of the annotations are not relevant in Covid-19 related texts. Therefore, we only used part of AGAC corpus with 8 labels:

  • 4 biological-concept labels annotate the biological proceses: Molecular Phsiological Activity, Interaction, Pathway, Cell Phsiological Activity.
  • 2 regulatory-concept labels annotate the change: PosReg, NegReg.
  • 2 biological entities: Enzyme, Protein.

Application

To investigate the changed biological processes and the related gene in Covid-19 literatures, we extracted the words that annotated by AGAC and the gene annotated by Litcovid which co- occurred in sentence-level. Figure at the left shows the biological processes in ACE2 gene, there is a strong relation between ACE2 and “bind” which is annotated as Interaction by AGAC. It means ACE2 related binding are concerned a lot in the Covid-19 research.

The sentence level co-occurrence gene with AGAC label.

Annotations download here.

Download

Cite us

Sizhuo Ouyang, Yuxing Wang, Kaiyin Zhou, Jingbo Xia*. LitCovid-AGAC: Cellular and Molecular Level Annotation Data Set Based on Covid-19. Genomics and Informatics, 2021; 19(3): e23.

Contact Us

College of Informatics
Huazhong Agricultural Univ
Wuhan, Hubei 430070
China
Jingbo Xia, xiajingbo.math@gmail.com
AGAC Teams

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