Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: a cross-sectional analysis within a population-based birth cohort
Open accessAbstract
BACKGROUND: The relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We propose that the discrepancies arise in part because allergic sensitisation may not be a single entity (as considered conventionally) but a collection of several different classes of sensitisation. We hypothesise that pairings between immunoglobulin E (IgE) antibodies to individual allergenic molecules (components), rather than IgE responses to 'informative' molecules, are associated with increased risk of asthma. METHODS AND FINDINGS: In a cross-sectional analysis among 461 children aged 11 years participating in a population-based birth cohort, we measured serum-specific IgE responses to 112 allergen components using a multiplex array (ImmunoCAP Immuno‑Solid phase Allergy Chip [ISAC]). We characterised sensitivity to 44 active components (specific immunoglobulin E [sIgE] > 0.30 units in at least 5% of children) among the 213 (46.2%) participants sensitised to at least one of these 44 components. We adopted several machine learning methodologies that offer a powerful framework to investigate the highly complex sIgE-asthma relationship. Firstly, we applied network analysis and hierarchical clustering (HC) to explore the connectivity structure of component-specific IgEs and identify clusters of component-specific sensitisation ('component clusters'). Of the 44 components included in the model, 33 grouped in seven clusters (C.sIgE-1-7), and the remaining 11 formed singleton clusters. Cluster membership mapped closely to the structural homology of proteins and/or their biological source. Components in the pathogenesis-related (PR)-10 proteins cluster (C.sIgE-5) were central to the network and mediated connections between components from grass (C.sIgE-4), trees (C.sIgE-6), and profilin clusters (C.sIgE-7) with those in mite (C.sIgE-1), lipocalins (C.sIgE-3), and peanut clusters (C.sIgE-2). We then used HC to identify four common 'sensitisation clusters' among study participants: (1) multiple sensitisation (sIgE to multiple components across all seven component clusters and singleton components), (2) predominantly dust mite sensitisation (IgE responses mainly to components from C.sIgE-1), (3) predominantly grass and tree sensitisation (sIgE to multiple components across C.sIgE-4-7), and (4) lower-grade sensitisation. We used a bipartite network to explore the relationship between component clusters, sensitisation clusters, and asthma, and the joint density-based nonparametric differential interaction network analysis and classification (JDINAC) to test whether pairwise interactions of component-specific IgEs are associated with asthma. JDINAC with pairwise interactions provided a good balance between sensitivity (0.84) and specificity (0.87), and outperformed penalised logistic regression with individual sIgE components in predicting asthma, with an area under the curve (AUC) of 0.94, compared with 0.73. We then inferred the differential network of pairwise component-specific IgE interactions, which demonstrated that 18 pairs of components predicted asthma. These findings were confirmed in an independent sample of children aged 8 years who participated in the same birth cohort but did not have component-resolved diagnostics (CRD) data at age 11 years. The main limitation of our study was the exclusion of potentially important allergens caused by both the ISAC chip resolution as well as the filtering step. Clustering and the network analyses might have provided different solutions if additional components had been available. CONCLUSIONS: Interactions between pairs of sIgE components are associated with increased risk of asthma and may provide the basis for designing diagnostic tools for asthma.
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Studies using component-resolved diagnostics have identified different cross-sectional and longitudinal patterns of component-specific IgE responses associated with different risk of asthma presence, persistence and severity in children.
“Further studies using component-resolved diagnostics rather than standard skin and blood tests to whole allergen extracts have identified different cross-sectional and longitudinal patterns of component-specific IgE responses associated with different risk of asthma presence, persistence and severity in children (90–93).”
From Epidemiology of Asthma in Children and Adults (2019) · cites this paper as [93]
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Microarray-Based Allergy Diagnosis: Quo Vadis?
Huang, H., Campana, R., Akinfenwa, O., Curin, M., Sarzsinszky, E., Васильевна, К. А., Riabova, K. A., Karaulov, A. V., Niespodziana, K., Elisyutina, O. G., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.594978 · Cited by 35 · see citing works
Noninvasive and minimally invasive techniques for the diagnosis and management of allergic diseases
Baumann, R., Untersmayr, E., Zissler, U. M., Eyerich, S., Adcock, I. M., Brockow, K., Biedermann, T., Ollert, M. W., Chaker, A. M., Pfaar, O., et al. (2020) · Allergy · doi:10.1111/all.14645 · Cited by 31 · see citing works
Chen, H., He, Y., Ji, J., & Shi, Y. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01162 · Cited by 31 · see citing works
“The parameters βij denote the differential dependency patterns between condition-specific groups (32).”
BackgroundUtility of biomarkers in the diagnosis and monitoring of asthmatic children
Xepapadaki, P. V., Adachi, Y., Beltrán, C. F. P., El-Sayed, Z., Gómez, R. M., Hossny, E. M., Filipović, I., Souëf, P. N. L., Morais‐Almeida, M., Miligkos, M., et al. (2022) · World Allergy Organization Journal · doi:10.1016/j.waojou.2022.100727 · Cited by 30 · see citing works
“The major advantage of such comprehensive analysis is the identification of clinically relevant sensitizations, which supports asthma diagnosis.(68) As with other allergy–related assays, positive results should be interpreted in the presence of asthma-related symptoms, although sensitization to major allergen proteins…”
BackgroundHou, X., Luo, W., Wu, L., Chen, Y., Li, G., Zhang, R., Zhang, H., Wu, J., Sun, Y., Xu, L., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101349 · Cited by 27 · see citing works
Machine learning in asthma research: moving toward a more integrated approach
Fontanella, S., Cucco, A., & Čustović, A. (2021) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2021.1894133 · Cited by 25 · see citing works
“…hypothesis-generating techniques have been extensively applied to the discovery of asthma subtypes and their pathophysiological mechanism [7–18], its relation with allergic sensitization [19–23] and environmental exposures [24–29], the discovery of biomarkers [30–40] and asthma prediction [41–46].”
BackgroundThe Molecular Allergen Recognition Profile in China as Basis for Allergen-Specific Immunotherapy
Dsouza, N., Weber, M., Sarzsinszky, E., Vrtala, S., Curin, M., Schaar, M., Garib, V., Focke‐Tejkl, M., Li, Y., Jones, R., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.719573 · Cited by 24 · see citing works
From preschool wheezing to asthma: Immunological determinants
Laubhahn, K., & Schaub, B. (2023) · Pediatric Allergy and Immunology · doi:10.1111/pai.14038 · Cited by 23 · see citing works
Pediatric asthma comorbidities: Global impact and unmet needs
Hossny, E. M., Adachi, Y., Anastasiou, E., Badellino, H., Čustović, A., El-Owaidy, R. H., El-Sayed, Z., Filipović, I., Gómez, R. M., Kalaycı, Ö., et al. (2024) · World Allergy Organization Journal · doi:10.1016/j.waojou.2024.100909 · Cited by 23 · see citing works
Siroux, V., Boudier, A., Bousquet, J., Dumas, O., Just, J., Moual, N. L., Nadif, R., Varraso, R., Valenta, R., & Pin, I. (2021) · Allergy · doi:10.1111/all.14987 · Cited by 22 · see citing works
Combining Allergen Components Improves the Accuracy of Peanut Allergy Diagnosis
Hemmings, O., Niazi, U. H. K., Kwok, M., Radulović, S., Toit, G. D., Lack, G., & Santos, A. F. (2021) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2021.08.029 · Cited by 22 · see citing works
The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment
Khan, M., Banerjee, S., Muskawad, S., Maity, R., Chowdhury, S. R., Ejaz, R., Kuuzie, E., & Satnarine, T. (2024) · Current Allergy and Asthma Reports · doi:10.1007/s11882-024-01152-y · Cited by 19 · see citing works
“Furthermore, the networks of interactions identified are linked to asthma diagnosis and severity [18, 28, 29].”
Dust-mite inducing asthma: what advice can be given to patients?
Čustović, A., Murray, C. S., & Simpson, A. (2019) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2019.1651647 · Cited by 17 · see citing works
Hadebe, S., & Brombacher, F. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02696 · Cited by 18 · see citing works
“The use of artificial intelligence to predict IgE specific fungal allergens will likely have a bigger impact in hospital settings, particularly in prospective birth cohorts (169).”
BackgroundUnderstanding the heterogeneity of childhood allergic sensitization and its relationship with asthma
Čustović, A., Custovic, D., & Fontanella, S. (2024) · Current Opinion in Allergy and Clinical Immunology · doi:10.1097/aci.0000000000000967 · Cited by 17 · see citing works
Childhood CCL18, CXCL10 and CXCL11 levels differentially relate to and predict allergy development
Huoman, J., Haider, S., Simpson, A., Murray, C. S., Čustović, A., & Jenmalm, M. C. (2021) · Pediatric Allergy and Immunology · doi:10.1111/pai.13574 · Cited by 16 · see citing works
Tyrak, K. E., Pajdzik, K., Konduracka, E., Ćmiel, A., Jakieła, B., Celejewska‐Wójcik, N., Trąd, G., Kot, A., Urbanska, A. S., Zabiegło, E., et al. (2020) · Allergy · doi:10.1111/all.14214 · Cited by 15 · see citing works
“Asthma duration (years) 10 (6-17) 11 (5-20) .”
Allergen extract‐ and component‐based diagnostics in children of the ALLIANCE asthma cohort
Skevaki, C. L., Tafo, P., Eiringhaus, K., Timmesfeld, N., Weckmann, M., Happle, C., Nelson, P. P., Maison, N., Schaub, B., Ricklefs, I., et al. (2021) · Clinical & Experimental Allergy · doi:10.1111/cea.13964 · Cited by 15 · see citing works
Czolk, R., Ruiz‐Castell, M., Hunewald, O. E., Wanniang, N., Coroller, G. L., Hilger, C., Vaillant, M., Fagherazzi, G., Morel‐Codreanu, F., Ollert, M. W., et al. (2023) · Clinical and Translational Allergy · doi:10.1002/clt2.12292 · Cited by 13 · see citing works
“Another study on unsupervised clustering described such complex molecular profiles yet to be present in childhood among other heterogeneous allergen profiles.(27) Indeed, our cluster 0 represents a participant group with intensive molecular”
BackgroundMethodInfluentialPhenotype and endotype based treatment of preschool wheeze
Salehian, S., Fleming, L., Saglani, S., & Čustović, A. (2023) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2023.2271832 · Cited by 13 · see citing works
“Unfortunately, confirmation of sensitization using skin prick tests (SPTs) and measurement of serum sIgE do not prove that patient’s symptoms are caused by allergy or T2-related mechanisms [69,70].”
BackgroundPrecision medicine in the allergy clinic: the application of component resolved diagnosis
Panaitescu, C. B., Haidar, L., Buzan, M. R., Grijincu, M., Spanu, D. E., Cojanu, C., Laculiceanu, A. G., Bumbăcea, R. S., & Agache, I. O. (2022) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2022.2034501 · Cited by 13 · see citing works
“Artificial intelligence-aided interpretation of the results, such as pairings between IgE antibodies to individual allergenic molecules (components), rather than IgE responses to ‘informative’ molecules, can be used to predict the increased risk of asthma [207].”
BackgroundBarriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review
Assadullah, M. M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3530598 · Cited by 13 · see citing works
Component‐specific clusters for diagnosis and prediction of allergic airway diseases
Howard, R., Fontanella, S., Simpson, A., Murray, C. S., Čustović, A., & Rattray, M. (2024) · Clinical & Experimental Allergy · doi:10.1111/cea.14468 · Cited by 12 · see citing works
“This approach has identified clusters of c-sIgEs to multiple allergenic proteins associated with asthma diagnosis 25,26 and severity 27 in cross-sectional studies, as well as asthma development in longitudinal studies.”
BackgroundInfluentialLejeune, S., Bouazza, N., Nicaise‐Roland, P., Jolaine, V., Roditis, L., Marguet, C., Amat, F., Berger, P., Fayon, M. J., Dubus, J. ‐., et al. (2024) · Pediatric Allergy and Immunology · doi:10.1111/pai.14112 · Cited by 10 · see citing works
Lee, E. M., Lee, S., Park, M. J., & Hong, S. (2020) · Experimental and Molecular Pathology · doi:10.1016/j.yexmp.2020.104467 · Cited by 9 · see citing works
Filiou, A., Holmdahl, I., Asarnoj, A., van Hage, M., Ekenkrantz, T., Rydell, N., Sjölander, A., Hammar, K. S., Hedlin, G., Konradsen, J. R., et al. (2022) · International Archives of Allergy and Immunology · doi:10.1159/000521324 · Cited by 9 · see citing works
Bourgoin‐Heck, M., Goldnadel, V. W., Chantran, Y., Saf, S., Guiddir, T., Amat, F., Rancière, F., Momas, I., Wanin, S., Rose, T., et al. (2024) · Pediatric Allergy and Immunology · doi:10.1111/pai.70014 · Cited by 8 · see citing works
Predictors of Airway Hyperreactivity in House Dust Mite Allergic Patients
Pampuch, A., Milewski, R. J., Rogowska, A., & Kowal, K. (2019) · Advances in respiratory medicine · doi:10.5603/arm.2019.0025 · Cited by 8 · see citing works
Breaking Down Silos in Asthma Research: The Case for an Integrated Approach
Haider, S., & Čustović, A. (2019) · EMJ Innovation · doi:10.33590/emjinnov/10314913 · Cited by 8 · see citing works
Rhinitis Phenotypes Based on Multimorbidities
Cruz, Á. A., & Bousquet, J. (2024) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2024.04.013 · Cited by 7 · see citing works
Santos, J., Dias, C. M., & Filho, A. D. P. C. (2020) · Health Policy and Technology · doi:10.1016/j.hlpt.2020.11.002 · Cited by 7 · see citing works
Association between allergen component sensitisation and clinical allergic disease in children
Brand, P. L. P., Brohet, R. M., Schwantje, O., & Dikkeschei, L. D. (2022) · Allergologia et Immunopathologia · doi:10.15586/aei.v50i2.598 · Cited by 6 · see citing works
Patterns of Respiratory Symptoms and Asthma Diagnosis in School‐Age Children: Three Birth Cohorts
Cucco, A., Simpson, A., Haider, S., Murray, C. S., Turner, S., Cullinan, P., Filippi, S. L., Fontanella, S., Čustović, A., & STELAR/UNICORN Investigators (2025) · Allergy · doi:10.1111/all.16617 · Cited by 6 · see citing works
“The process of asthma disaggregation can be facilitated using hypothesis-generating unbiased approaches [5–13].”
Early priming of asthma and respiratory allergies: Future aspects of prevention
Wahn, U., Lau, S., Eigenmann, P., Melén, E., Krauss‐Etschmann, S., Lex, C., Matricardi, P. M., Schaub, B., Halken, S., Ege, M. J., et al. (2022) · Pediatric Allergy and Immunology · doi:10.1111/pai.13773 · Cited by 5 · see citing works
Inoue, N., Hirota, T., Hatano, A., Nakano, M., Nakashima, D., Nakayama, T., Tamari, M., & Yoshikawa, M. (2023) · Auris Nasus Larynx · doi:10.1016/j.anl.2023.09.007 · Cited by 5 · see citing works
Yang, M. X., Meng, Y., Hao, W., Zhang, J., Liu, J., Wu, L., Lin, B., Liu, Y., Zhang, Y., Yu, X., et al. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.111829 · Cited by 5 · see citing works
Cucco, A., Simpson, A., Murray, C. S., Čustović, A., & Fontanella, S. (2025) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2025.02.012 · Cited by 4 · see citing works
Machine learning analysis of humoral and cellular responses to SARS-CoV-2 infection in young adults
Marcinkevičs, R., Silva, P. N., Hankele, A., Dörnte, C., Kadelka, S., Csik, K., Godbersen, S., Goga, A., Hasenöhrl, L., Hirschi, P., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1158905 · Cited by 4 · see citing works
Platts‐Mills, T. A. E., & Perzanowski, M. S. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002696 · Cited by 4 · see citing works
Structure Analysis of a Pathogenesis-Related 10 Protein fromGardenia jasminoides
Gao, L. (2019) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/242/4/042005 · Cited by 3 · see citing works
Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review
Bashir, M. B. A., Milani, G. P., Cosmi, V. D., Mazzocchi, A., Zhang, G.-Q., Basna, R., Hedman, L., Lindberg, A., Ekerljung, L., Axelsson, M., et al. (2025) · Journal of Asthma and Allergy · doi:10.2147/jaa.s463572 · Cited by 3 · see citing works
Machine learning in allergy research: A bibliometric review
Kong, E., Cucco, A., Čustović, A., & Fontanella, S. (2025) · Immunology Letters · doi:10.1016/j.imlet.2025.107088 · Cited by 4 · see citing works
From personalized medicine to precision medicine
Collecchia, G., & Gobbi, R. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14054-9.00004-1 · Cited by 2 · see citing works
Allergen immunotherapy based on molecular diagnostics: pediatric aspects
Matricardi, P. M. (2025) · Allergo Journal International · doi:10.1007/s40629-024-00318-4 · Cited by 3 · see citing works
Li, Y., Ye, Q., Lu, Y., Yang, K., Zhang, P., Chen, C., Zhou, M., Feng, P., & Chen, Z. (2025) · Clinical and Translational Allergy · doi:10.1002/clt2.70030 · Cited by 2 · see citing works
Is asthma's heterogeneity too vast to use traditional phenotyping for modern biologic therapies?
van der Burg, N. M. D., & Tufvesson, E. (2023) · Respiratory Medicine · doi:10.1016/j.rmed.2023.107211 · Cited by 2 · see citing works
Brindisi, G., Cipriani, F., Potapova, E., Tripodi, S., Panetta, V., Bernardini, R., Caffarelli, C., Casani, A., Cervone, R., Chini, L., et al. (2024) · World Allergy Organization Journal · doi:10.1016/j.waojou.2024.100975 · Cited by 2 · see citing works
“32 Another recent study investigated the role of CRD in identifying clinically relevant sensitizations in children at high risk of developing asthma.”
BackgroundThe Epidemiology of Severe Childhood Asthma
Čustović, A., & Martínez, F. D. (2019) · Springer eBooks · doi:10.1007/978-3-030-27431-3_1 · Cited by 2 · see citing works
Digital Allergology: State of the Art, 2024
Matricardi, P. M., & Pfaar, O. (2025) · Health informatics · doi:10.1007/978-3-031-71021-6_1 · Cited by 1 · see citing works
Machine Learning for the Management of Allergies and Asthma in Childhood
Čustović, A., Custovic, D., & Fontanella, S. (2025) · Health informatics · doi:10.1007/978-3-031-71021-6_13 · Cited by 1 · see citing works
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Allergen-Immuntherapie auf der Grundlage einer molekularen Allergiediagnostik: pädiatrische Aspekte
Matricardi, P. M. (2025) · Allergo Journal · doi:10.1007/s15007-025-6443-1 · Cited by 1 · see citing works
“Die Gruppe der Kinder, die sowohl auf Gräserpollen-als auch auf Milbenallergene polymolekular sensibilisiert sind, stellt einen abgegrenzten und gut differenzierten Cluster dar, der in anderen Geburtskohortenstudien bestätigt wurde [39, 41].”
Прогнозирование тяжелого течения бронхиальной астмы у детей
Klymenko, V., & Kozhyna, O. (2019) · CHILD`S HEALTH · doi:10.22141/2224-0551.14.5.2019.177406
Huoman, J. (2020) · Linköping University medical dissertations · doi:10.3384/diss.diva-170575
Huoman, J., Haider, S., Simpson, A., Murray, C. S., Čustović, A., & Jenmalm, M. C. (2020) · Research Square · doi:10.21203/rs.3.rs-113276/v1
Ciência de dados e politicas públicas de saúde: exemplos práticos
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Epidemiology of Allergic Diseases
Čustović, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-80912-2.00003-2
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Specific immunoglobulin E profiles in sensitized Swedish and Spanish children with severe asthma
Martí‐Garrido, J., Konradsen, J. R., Hedlin, G., van Hage, M., Nieto, A., Nieto, M., Uixera, S., & Mazón, Á. (2023) · doi:10.37349/eaa.2023.00019
Allergic diseases in children: a modern view on the problem
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Bayesian Neural Networks for predicting the severity of symptoms: a case study
Belciug, S., & MIHAI, T. A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.08.126
Molecular Diagnosis in Pediatric Allergy
Matricardi, P. M., & Santos, A. F. (2024) · Springer eBooks · doi:10.1007/978-3-031-71283-8_23
Exploring geographic differences in IgE response through network and manifold analyses
Cucco, A., Pearce, N. E., Simpson, A., Pembrey, L., Mpairwe, H., Figueiredo, C. A., Cooper, P. J., Douwes, J. E., Brooks, C., Adcock, I. M., et al. (2025) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2025.05.032
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Clustering lung function and symptom profiles for asthma risk stratification
Cucco, A., Simpson, A., Murray, C. S., Roberts, G. C., Holloway, J. W., Arshad, S. H., Čustović, A., & Fontanella, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-32977-w
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“Therefore, it lies in hypothesis generation and clustering to identify a minimal informative set of specific IgE components for phenotyping and risk stratification, similar to prior work in asthma endo-typing [27 seasons [28].”
Disrupted barriers, evolved risks: toward precision One Health in arthropod allergy
Li, D., Zhang, W.-F., Deng, Y., Duan, D., Liu, W., Fu, Y.-T., Meng, Q., Zeng, Y.-K., Elsheikha, H. M., Liu, L., et al. (2026) · Trends in Parasitology · doi:10.1016/j.pt.2026.05.014
Molecular sensitization clusters in adult atopy: an ALEX2-based analysis from Eastern Europe
Chera, A., Ali, S., Chau, C., Hu, B., Villanueva, A. N., Guttman‐Yassky, E., Bucur, O., & Bumbăcea, R. S. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1960324
- Authors
- Fontanella, S., Frainay, C., Murray, C. S., Simpson, A., & Custovic, A.
- Affiliations
- Imperial College London (GB)
- Toxicologie Alimentaire (FR)
- Manchester Academic Health Science Centre (GB)
- Venue
- PLoS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-11-13
- Volume
- 15
- Issue
- 11
- Pages
- e1002691
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC6233916
- OpenAlex
- W2900723459
- Cited by
- 91
- References
- 47
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 15 · 2023: 12 · 2022: 10 · 2021: 11 · 2020: 12 · 2019: 12 · 2018: 3
- FWCI
- 6.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Medical Research Council (MR/L012693/1, MR/K002449/1)
- Topics
- Health Sciences › Medicine › Physiology › Asthma and respiratory diseases
- Health Sciences › Medicine › Immunology and Allergy › Allergic Rhinitis and Sensitization
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Occupational exposure and asthma
- Keywords
- asthma, specific IgE, allergen sensitization, allergen components, hierarchical clustering, profilin, lipocalin, asthma risk, children, birth cohort, machine learning, network analysis, ImmunoCAP ISAC, asthma phenotypes
- MeSH terms
- Machine Learning, Allergens, Asthma, Child, Cross-Sectional Studies, England, Female, Humans, Hypersensitivity, Immunoglobulin E, Male, Risk Factors, Biomarkers, Cluster Analysis, Risk Assessment, Data Mining
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