. 2022 Dec 20;3(12):100857.

doi: 10.1016/j.xcrm.2022.100857.


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Anuradha Ray et al.

Cell Rep Med.



There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.


artificial intelligence; asthma; endotypes; machine learning; molecular phenotypes.

Conflict of interest statement

Declaration of interests A.R. has a research agreement with Pieris Pharmaceuticals. J.D. is a consultant for Seromyx Systems. S.E.W. is a consultant for AstraZeneca, Glaxo Smith-Kline, and Sanofi. She is also involved in clinical trials being run by Knopp, Sanofi, and AstraZeneca. She has a research agreement with Pieris Pharmaceuticals.