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J Immunol. 2019 Aug 1;203(3):749-759. doi: 10.4049/jimmunol.1900033. Epub 2019 Jun 14.

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

Author information

1
Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94304; info@adrianatomic.com.
2
Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford OX3 9DU, United Kingdom.
3
Independent researcher, Palo Alto, CA 94303.
4
Human Immune Monitoring Center, Stanford University, Stanford, CA 94304.
5
Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94304.
6
Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94304.
7
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94304; and.
8
Howard Hughes Medical Institute, Stanford University, Stanford, CA 94304.

Abstract

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.

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