In:
Science Translational Medicine, American Association for the Advancement of Science (AAAS), Vol. 14, No. 628 ( 2022-01-19)
Abstract:
As we gain more insight into the drivers of coronavirus disease 2019 (COVID-19), it is essential that these drivers be understood in the context of different populations, including those thought to be at low risk of developing severe disease. Here, Carapito et al. used a multi-omics approach to identify drivers of critical COVID-19 in a young, comorbidity-free patient cohort. The authors used an ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling to identify multiple candidate driver genes, including the metalloprotease, ADAM9 . Together, these findings suggest that drivers of critical COVID-19, and thus treatment, may differ based on the cohort.
Type of Medium:
Online Resource
ISSN:
1946-6234
,
1946-6242
DOI:
10.1126/scitranslmed.abj7521
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2022