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SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses

View ORCID ProfileAdriana Tomic, Ivan Tomic, Yael Rosenberg-Hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis
doi: https://doi.org/10.1101/545186
Adriana Tomic
1Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94304, USA.
2Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford OX3 9DU, UK.
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  • For correspondence: mmdavis@stanford.edu atomic@stanford.edu
Ivan Tomic
3Independent Researcher, Electronic address: .
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  • For correspondence: info@ivantomic.com
Yael Rosenberg-Hasson
4Human Immune Monitoring Center, Stanford University, Stanford, CA 94304, USA.
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Cornelia L. Dekker
5Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94304, USA.
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Holden T. Maecker
4Human Immune Monitoring Center, Stanford University, Stanford, CA 94304, USA.
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Mark M. Davis
1Institute of Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94304, USA.
6Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94304, USA.
7Howard Hughes Medical Institute, Stanford University, Stanford, CA 94304, USA.
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  • For correspondence: mmdavis@stanford.edu atomic@stanford.edu
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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. Here, we developed Sequential Iterative Modelling “OverNight” or 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 antibody response to influenza antigens. 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 10, 2019.
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SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses
Adriana Tomic, Ivan Tomic, Yael Rosenberg-Hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis
bioRxiv 545186; doi: https://doi.org/10.1101/545186
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SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses
Adriana Tomic, Ivan Tomic, Yael Rosenberg-Hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis
bioRxiv 545186; doi: https://doi.org/10.1101/545186

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