Mining the Associations between V(D)J Gene Segments and COVID-19 Disease Characteristics

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Yu Zhao
  • Yidan Zhang
  • Fan Yang
  • Lei Duan
  • Jianhua Yao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine
EditorsYufei Huang, Lukasz Kurgan, Feng Luo Luo, Xiaohua Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages608-613
ISBN (Electronic)9781665401265
ISBN (Print)9781665429825
Publication statusPublished - Dec 2021

Publication series

NameProceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM

Conference

Title2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021)
LocationVirtual
PlaceUnited States
CityHouston
Period9 - 12 December 2021

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.

Research Area(s)

  • association mining, COVID-19, immune repertoires, machine learning, multiple factor analysis, T cell receptor

Citation Format(s)

Mining the Associations between V(D)J Gene Segments and COVID-19 Disease Characteristics. / Zhao, Yu; Zhang, Yidan; Huang, Zhi-An; Yang, Fan; Duan, Lei; Yao, Jianhua.

Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo Luo; Xiaohua Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 608-613 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review