ArticleScientific reports2019
Establishing a many-cytokine signature via multivariate anomaly detection.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Transforming cytokine diagnostics: AI, multiplexing, and point-of-care biosensing technologies.Mikrochimica acta · 2025Review
- Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables.Frontiers in neurology · 2025Article
- Cytokine production by newborns: influence of sex and season of birth.Pediatric research · 2023Article
- Human differentiated eosinophils release IL-13 in response to IL-33 stimulation.Frontiers in immunology · 2022Article
- Cytokine Patterns in Maternal Serum From First Trimester to Term and Beyond.Frontiers in immunology · 2021Article
- CytokineExplore: An Online Tool for Statistical Analysis of Cytokine Concentration Datasets.Journal of inflammation research · 2020Article
- Multivariate Comparison of Cytokine Profiles for Normal- and Low-Bone-Density Subjects.Diagnostics (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Establishing a cytokine signature associated to some medical condition is an important task in immunology. Increasingly, large numbers of cytokines are used for signatures, via lists of reference ranges for each individual cytokine or ratios of cytokines. Here we argue that this common approach has weaknesses, especially when many different cytokines are analysed. Instead, we propose that establishing signatures can be framed as a multivariate anomaly detection problem, and hence exploit the many statistical methods available for this. In this framework, whether or not a given subject's profile matches the cytokine signature of some condition is determined by whether or not the profile is typical of reference samples of that condition, as judged by an anomaly detection algorithm. We examine previously published cytokine data sets associated to pregnancy complications, brain tumours, and rheumatoid arthritis, as well as normal healthy control samples, and test the performance of a range of anomaly detection algorithms on these data, identifying the best performing methods. Finally, we suggest that this anomaly detection approach could be adopted more widely for general multi-biomarker signatures.
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Registered trials
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