SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have diffic...

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Bibliographic Details
Published in:The Journal of immunology (1950) 2019-08, Vol.203 (3), p.749-759
Main Authors: Tomic, Adriana, Tomic, Ivan, Rosenberg-Hasson, Yael, Dekker, Cornelia L, Maecker, Holden T, Davis, Mark M
Format: Article
Language:English
Subjects:
Adolescent
Adult
Algorithms
CD4-Positive T-Lymphocytes - immunology
CD8-Positive T-Lymphocytes - immunology
Child
Data Mining
Datasets as Topic
Female
Humans
Influenza Vaccines - immunology
Influenza, Human - immunology
Influenza, Human - prevention & control
Influenza, Human - virology
Machine Learning
Male
Systems Immunology
T-Lymphocyte Subsets - immunology
Vaccination
Young Adult
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Summary:Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4 and CD8 T cell subsets strongly associated with a robust Ab response to influenza Ags. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.
ISSN:0022-1767
1550-6606
DOI:10.4049/jimmunol.1900033