TY - JOUR
T1 - Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire
AU - Zhao, Yu
AU - He, Bing
AU - Xu, Zhimeng
AU - Zhang, Yidan
AU - Zhao, Xuan
AU - Huang, Zhi An
AU - Yang, Fan
AU - Wang, Liang
AU - Duan, Lei
AU - Song, Jiangning
AU - Yao, Jianhua
N1 - Publisher Copyright:
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2023/1/19
Y1 - 2023/1/19
N2 - 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 https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.
AB - 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 https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.
KW - artificial intelligence
KW - COVID-19
KW - diagnosis
KW - prognosis
KW - TCR repertoire
UR - http://www.scopus.com/inward/record.url?scp=85147044692&partnerID=8YFLogxK
U2 - 10.1093/bib/bbac555
DO - 10.1093/bib/bbac555
M3 - Article
C2 - 36567255
AN - SCOPUS:85147044692
SN - 1477-4054
VL - 24
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 1
M1 - bbac555
ER -