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...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
American Association of Immunologists
2019
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| _version_ | 1826278120656732160 |
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| author | Tomic, A Tomic, I Rosenberg-Hasson, Y Dekker, C Maecker, H Davis, M |
| author_facet | Tomic, A Tomic, I Rosenberg-Hasson, Y Dekker, C Maecker, H Davis, M |
| author_sort | Tomic, A |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T23:39:12Z |
| format | Journal article |
| id | oxford-uuid:6eb50736-06d5-4b4d-b754-dc828e6f4739 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:39:12Z |
| publishDate | 2019 |
| publisher | American Association of Immunologists |
| record_format | dspace |
| spelling | oxford-uuid:6eb50736-06d5-4b4d-b754-dc828e6f47392022-03-26T19:26:13ZSIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responsesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6eb50736-06d5-4b4d-b754-dc828e6f4739EnglishSymplectic Elements at OxfordAmerican Association of Immunologists2019Tomic, ATomic, IRosenberg-Hasson, YDekker, CMaecker, HDavis, MMachine 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. |
| spellingShingle | Tomic, A Tomic, I Rosenberg-Hasson, Y Dekker, C Maecker, H Davis, M SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses |
| title | SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses |
| title_full | SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses |
| title_fullStr | SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses |
| title_full_unstemmed | SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses |
| title_short | SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses |
| title_sort | simon an automated machine learning system reveals immune signatures of influenza vaccine responses |
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