Biology subjects

Tomic, A.

Publications and source records attributed to Tomic, A..

2 recordsLinked to original sources

bioRxiv2019

The FluPRINT dataset: A multidimensional analysis of the influenza vaccine imprint on the immune system

Recent advances in machine learning have allowed identification of molecular and cellular factors that underly successful antibody responses to influenza vaccines. Results of these studies have revealed the high level of complexity necessary to establish influenza immunity, and many different cellular and molecular components involved. However, identified correlates of protection fail to account for the majority of vaccinated cases across ages, cohorts, and influenza seasons. Major challenges arise from small sample sizes and from analysis of only one aspect of the biology such by using transcriptome data. The objective of the current study is to create a unified database, entitled FluPRINT, to enable a large-scale study exploring novel cellular and molecular underpinnings of successful immunity to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell subset phenotypes, and cytokine stimulations. FluPRINT, thus facilitates application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that drive successful influenza vaccination.

systems biology↗

bioRxiv2019

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 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.

immunology↗