{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T05:49:25Z","timestamp":1791179365829,"version":"4.1.0"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2013,10,16]],"date-time":"2013-10-16T00:00:00Z","timestamp":1381881600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pediatric Allergy Immunology"],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Identifying different patterns of allergens and understanding their predictive ability in relation to asthma and other allergic diseases is crucial for the design of personalized diagnostic tools.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Allergen\u2010IgE screening using Immuno<jats:styled-content style=\"fixed-case\">CAP ISAC<\/jats:styled-content><jats:sup>\u00ae<\/jats:sup> assay was performed at age 11\u00a0yrs in children participating a population\u2010based birth cohort. Logistic regression (<jats:styled-content style=\"fixed-case\">LR<\/jats:styled-content>) and nonlinear statistical learning models, including random forests (<jats:styled-content style=\"fixed-case\">RF<\/jats:styled-content>) and Bayesian networks (<jats:styled-content style=\"fixed-case\">BN<\/jats:styled-content>), coupled with feature selection approaches, were used to identify patterns of allergen responses associated with asthma, rhino\u2010conjunctivitis, wheeze, eczema and airway hyper\u2010reactivity (<jats:styled-content style=\"fixed-case\">AHR<\/jats:styled-content>, positive methacholine challenge). Sensitivity\/specificity and area under the receiver operating characteristic (<jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>) were used to assess model performance <jats:italic>via<\/jats:italic> repeated validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Serum sample for IgE measurement was obtained from 461 of 822 (56.1%) participants. Two hundred and thirty\u2010eight of 461 (51.6%) children had at least one of 112 allergen components IgE\u00a0&gt;\u00a00\u00a0<jats:styled-content style=\"fixed-case\">ISU<\/jats:styled-content>. The binary threshold &gt;0.3\u00a0<jats:styled-content style=\"fixed-case\">ISU<\/jats:styled-content> performed less well than using continuous IgE values, discretizing data or using other data transformations, but not significantly (p\u00a0=\u00a00.1). With the exception of eczema (<jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>~0.5), <jats:styled-content style=\"fixed-case\">LR<\/jats:styled-content>,<jats:styled-content style=\"fixed-case\"> RF<\/jats:styled-content> and <jats:styled-content style=\"fixed-case\">BN<\/jats:styled-content> achieved comparable <jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>, ranging from 0.76 to 0.82. Dust mite, pollens and pet allergens were highly associated with asthma, whilst pollens and dust mite with rhino\u2010conjunctivitis. Egg\/bovine allergens were associated with eczema.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>After validation, <jats:styled-content style=\"fixed-case\">LR<\/jats:styled-content>,<jats:styled-content style=\"fixed-case\"> RF<\/jats:styled-content> and <jats:styled-content style=\"fixed-case\">BN<\/jats:styled-content> demonstrated reasonable discrimination ability for asthma, rhino\u2010conjunctivitis, wheeze and <jats:styled-content style=\"fixed-case\">AHR<\/jats:styled-content>, but not for eczema. However, further improvements in threshold ascertainment and\/or value transformation for different components, and better interpretation algorithms are needed to fully capitalize on the potential of the technology.<\/jats:p><\/jats:sec>","DOI":"10.1111\/pai.12139","type":"journal-article","created":{"date-parts":[[2013,10,17]],"date-time":"2013-10-17T03:36:29Z","timestamp":1381980989000},"page":"71-79","source":"Crossref","is-referenced-by-count":53,"title":["Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach"],"prefix":"10.1111","volume":"25","author":[{"given":"Mattia C. 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