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

Yu Zhao, Yidan Zhang, Zhi-An Huang, Fan Yang, Lei Duan, Jianhua Yao

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.