Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire

Yu Zhao, Bing He, Zhimeng Xu, Yidan Zhang, Xuan Zhao, Zhi An Huang, Fan Yang, Liang Wang, Lei Duan, Jiangning Song, Jianhua Yao

Research output: Contribution to journalArticleResearchpeer-review


Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at The web server of VDJMiner is available at

Original languageEnglish
Article numberbbac555
Number of pages14
JournalBriefings in Bioinformatics
Issue number1
Publication statusPublished - 19 Jan 2023


  • artificial intelligence
  • COVID-19
  • diagnosis
  • prognosis
  • TCR repertoire

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