Table1_Pediatric Crohn's disease diagnosis aid via genomic analysis and machine learning.xlsx

IntroductionDetermination of pediatric Crohn's disease (CD) remains a major diagnostic challenge. However, the rapidly emerging field of artificial intelligence has demonstrated promise in developing diagnostic models for intractable diseases.
MethodsWe propose an artificial neural network model of 8 gene markers identified by 4 classification algorithms based on Gene Expression Omnibus database for diagnostic of pediatric CD.
ResultsThe model achieved over 85% accuracy and area under ROC curve value in both training set and testing set for diagnosing pediatric CD. Additionally, immune infiltration analysis was performed to address why these markers can be integrated to develop a diagnostic model.
ConclusionThis study supports further clinical facilitation of precise disease diagnosis by integrating genomics and machine learning algorithms in open-access database.

引言：小儿克罗恩病（pediatric Crohn's Disease, CD）的确诊仍是临床诊断的重大挑战。然而，快速兴起的人工智能领域在难治性疾病诊断模型开发方面已展现出应用潜力。
方法：本研究基于基因表达综合数据库（Gene Expression Omnibus, GEO），通过4种分类算法筛选出8个基因标志物，构建人工神经网络诊断模型以用于小儿CD的诊断。
结果：该模型在训练集与测试集的小儿CD诊断任务中，准确率与受试者工作特征曲线下面积（area under ROC curve, AUC）均超过85%。此外，本研究通过免疫浸润分析，阐释了这些标志物可被整合用于构建诊断模型的潜在机制。
结论：本研究证实，通过整合基因组学与机器学习算法，并依托开放获取数据库，可为精准疾病诊断的临床推广提供进一步支持。