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Mining the Associations between V(D)J Gene Segments and COVID-19 Disease Characteristics
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; : 608-613, 2021.
Article in English | Scopus | ID: covidwho-1722895
ABSTRACT
The emerging COVID-19 variants lead to a new wave of infections, spreading more rapidly with more severe illnesses. The adaptive immune system plays an essential role in the control and clearance of viral infection and influences clinical outcomes. However, the understanding of the adaptive immune responses to COVID-19 is not sufficient, which impedes the development progress of treatments and vaccines. To address this issue, we proposed a machine-learning-based method (termed as VDJ-Seg-Miner) to mine the underlying associations between the V(D)J gene segments of the T cell receptor in personalized immune repertoires and COVID-19 disease characteristics for immune system analysis. Our VDJ-Seg-Miner can interpretively reveal multiple associations between the V(D)J gene segments and COVID-19 disease characteristics and assign confidence scores to indicate its confidence in each revealed association. Furthermore, experimental results based on the real-world dataset suggested that the identified associations were highly consistent with those reported in previous work. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 Year: 2021 Document Type: Article