| Literature DB >> 30421126 |
Abstract
Asthma is a complex heterogeneous disorder with hereditary tendency and the most widely used therapy is inhalation of anti-inflammatory corticosteroids. But it has systemic side effects. If the chronic inflammation can be detected in early stage, the dosage of corticosteroids will be low and the side effects can be avoided. Therefore, to discover the early stage blood biomarkers for asthma, we analyzed the gene expression profiles in the blood of 77 moderate asthma patients and 87 healthy controls. With advanced feature selection methods, minimal Redundancy Maximal Relevance and Incremental Feature Selection, we identified 31 genes, such as MYD88, ZFP36, CCR3 and CYP3A5, as the optimal asthma biomarker. The sensitivity, specificity and accuracy of the 31-gene Support Vector Machine predictor evaluated with Leave-One-Out Cross Validation were 0.870, 0.816 and 0.841, respectively. Through literature survey, many biomarker genes have asthma associated functions. Our results not only provided the easy-to-apply blood gene expression biomarkers for early detection of asthma, but also an explainable qualitative model with biological significance.Entities:
Keywords: Asthma; Blood gene expression; Incremental Feature Selection; Minimal Redundancy Maximal Relevance; Support Vector Machine
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Year: 2018 PMID: 30421126 DOI: 10.1007/s11033-018-4463-6
Source DB: PubMed Journal: Mol Biol Rep ISSN: 0301-4851 Impact factor: 2.316