ArticleFrontiers in medicine2024
Genetic biomarker prediction based on gender disparity in asthma throughout machine learning.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- AI-based prediction of aspirin-exacerbated respiratory disease using nasal epithelial mRNA expression profiles.The journal of allergy and clinical immunology. Global · 2026Article
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Authors and funding
13 authors.
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Abstract
Background: Asthma is a chronic respiratory condition affecting populations worldwide, with prevalence ranging from 1-18% across different nations. Gender differences in asthma prevalence have attracted much attention. Purpose: The aim of this study was to investigate biomarkers of gender differences in asthma prevalence based on machine learning. Method: The data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males and 335 females. Theses samples were divided into male group and female group, respectively. Grid search and cross-validation were employed to adjust model parameters for support vector machine, random forest, decision tree and logistic regression model. Accuracy, precision, recall, and F Result: In datasets GSE76262 and GSE69683, support vector machine, random forest, logistic regression, and decision tree all achieve 100% accuracy, precision, recall, and F Conclusion: XIST serves as a genetic biomarker for gender differences in the prevalence of asthma.
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