Background. Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN). Methods. Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model. Results. The methylation analysis tool (ChAMP) considered DECs that were up-regulated ( =121) and down-regulated ( =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model. Conclusion. Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.

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基于随机森林和神经网络的哮喘识别甲基化诊断模型

背景。哮喘作为一种慢性疾病显着影响人类的生命和健康。哮喘的传统治疗有几个局限性。人工智能有助于癌症治疗，也可能加速我们对哮喘机制的理解。我们旨在使用人工神经网络 (ANN) 开发一种新的哮喘临床诊断模型。方法. 数据集（GSE85566、GSE40576 和 GSE13716）从 Gene Expression Omnibus (GEO) 下载，并通过基因本体论 (GO) 和京都基因和基因组百科全书 (KEGG) 分析确定了差异表达的 CpGs (DECs)。随机森林 (RF) 和 ANN 算法进一步识别基因特征并建立临床模型。此外，还使用了两个外部验证数据集（GSE40576 和 GSE137716）来验证模型的诊断能力。结果。甲基化分析工具 (ChAMP) 考虑了上调 (  =121) 和下调 ( =20)。GO 结果显示肌动蛋白细胞骨架组织和细胞-基质粘附、志贺菌病和血清素能突触的富集。RF（随机森林）分析确定了 10 个关键的 DEC（cg05075579、cg20434422、cg03907390、cg00712106、cg05696969、cg22862094、cg11733958、cg00328720 和 cg13570822）。ANN根据10个DECs构建了临床模型。在两个外部验证数据集（GSE40576 和 GSE137716）中，GSE137716 的曲线下面积 (AUC) 为 1.000，GSE40576 的 AUC 为 0.950，证实了模型的可靠性。结论。我们的研究结果为哮喘诊断和治疗提供了新的甲基化标志物和临床诊断模型。