Abstract
Background
Associations between childhood asthma phenotypes and genetic, immunological and environmental factors have been previously established. Yet, strategies to integrate high‐dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them.
Methods
We assembled questionnaire, diagnostic, genotype, microarray, RT‐qPCR, flow cytometry and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild‐to‐moderate allergic asthmatics and non‐allergic asthmatics. Based on data from 260 German children aged 4‐14 from our university outpatient clinic, we built a novel multi‐level prediction approach for asthma outcome which could deal with a present complex missing data structure.
Results
The optimal learning method was boosting based on all data sets, achieving an area‐underneath‐the‐receiver‐operating‐characteristic curve (AUC) for three classes of phenotypes of 0.81 (95%‐confidence interval (CI): 0.65‐0.94) using leave‐one‐out cross‐validation. Besides improving the AUC, our integrative multi‐level learning approach led to tighter CIs than using smaller complete predictor data sets (AUC=0.82[0.66‐0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN2 (protein kinase N2), PTK2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental).
Conclusion
Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multi‐level data‐based risk prediction settings, which typically suffer from incomplete data.
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