Challenges in interpreting allergen microarrays in relation to clinical symptoms: a machine learning approach.

Title: Challenges in interpreting allergen microarrays in relation to clinical symptoms: a machine learning approach.
Authors: Prosperi, MC
Belgrave, D
Buchan, I
Simpson, A
Custovic, A
Item Type: Journal Article
Abstract: 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.Allergen-IgE screening using ImmunoCAP ISAC(®) assay was performed at age 11 yrs in children participating a population-based birth cohort. Logistic regression (LR) and nonlinear statistical learning models, including random forests (RF) and Bayesian networks (BN), coupled with feature selection approaches, were used to identify patterns of allergen responses associated with asthma, rhino-conjunctivitis, wheeze, eczema and airway hyper-reactivity (AHR, positive methacholine challenge). Sensitivity/specificity and area under the receiver operating characteristic (AUROC) were used to assess model performance via repeated validation.Serum sample for IgE measurement was obtained from 461 of 822 (56.1%) participants. Two hundred and thirty-eight of 461 (51.6%) children had at least one of 112 allergen components IgE > 0 ISU. The binary threshold >0.3 ISU performed less well than using continuous IgE values, discretizing data or using other data transformations, but not significantly (p = 0.1). With the exception of eczema (AUROC~0.5), LR, RF and BN achieved comparable AUROC, 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-conjunctivitis. Egg/bovine allergens were associated with eczema.After validation, LR, RF and BN demonstrated reasonable discrimination ability for asthma, rhino-conjunctivitis, wheeze and AHR, 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.
Issue Date: 28-Feb-2014
Start Page: 71
End Page: 79
Journal / Book Title: Pediatr Allergy Immunol
Volume: 25
Issue: 1
Copyright Statement: © 2013 The Authors Pediatric Allergy and Immunology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian networks
airway hyper-reactivity
component-resolved diagnostics
feature selection
logistic regression
machine learning
random forests
Publication Status: Published
Conference Place: England
Appears in Collections:Department of Medicine
Faculty of Medicine

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