RT Journal Article
SR Electronic
T1 SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 545186
DO 10.1101/545186
A1 Adriana Tomic
A1 Ivan Tomic
A1 Yael Rosenberg-Hasson
A1 Cornelia L. Dekker
A1 Holden T. Maecker
A1 Mark M. Davis
YR 2019
UL http://biorxiv.org/content/early/2019/02/10/545186.abstract
AB 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.