Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Department of Biomedical and Surgical and Biomedical Sciences, Catania University, Catania, Italy.
J Craniofac Surg. 2024 Jun 1;35(4):1292-1297. doi: 10.1097/SCS.0000000000010111. Epub 2024 Jun 3.
Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at risk for AMI, has been one important research topic. According to recent research, AMI and PD interact via the systemic production of certain proinflammatory and anti-inflammatory cytokines, small signal molecules, and enzymes that control the onset and development of both disorders' chronic inflammatory reactions. This study uses machine learning to identify the interactome hub biomarker genes in acute myocardial infarction and periodontitis.
GSE208194 and GSE222883 were chosen for our research after a thorough search using keywords related to the study's goal from the gene expression omnibus (GEO) datasets. DEGs were identified from the GEOR tool, and the hub gene was identified using Cytoscape-cytohubba. Using expression values, Random Forest, Adaptive Boosting, and Naive Bayes, widgets-generated transcriptomics data, were labelled, and divided into 80/20 training and testing data with cross-validation. ROC curve, confusion matrix, and AUC were determined. In addition, Functional Enrichment Analysis of Differentially Expressed Gene analysis was performed.
Random Forest, AdaBoost, and Naive Bayes models with 99%, 100%, and 75% AUC, respectively. Compared to RF, AdaBoost, and NB classification models, AdaBoost had the highest AUC. Categorization algorithms may be better predictors than important biomarkers.
Machine learning model predicts hub and non-hub genes from genomic datasets with periodontitis and acute myocardial infarction.
急性心肌梗死（AMI）的风险与 C 反应蛋白（CRP）水平相关，这表明全身炎症在 AMI 发生之前就已经存在。研究牙周病（PD）的不同类型一直是一个重要的研究课题，PD 在 AMI 高危人群中极为常见。根据最近的研究，AMI 和 PD 通过全身产生某些促炎和抗炎细胞因子、小信号分子以及控制两种疾病慢性炎症反应发生和发展的酶相互作用。本研究使用机器学习来识别急性心肌梗死和牙周炎的互作网络枢纽生物标志物基因。
在使用与基因表达综合数据库（GEO）数据集相关的研究目标的关键字进行彻底搜索后，选择 GSE208194 和 GSE222883 进行我们的研究。使用 GEOR 工具确定 DEGs，使用 Cytoscape-cytohubba 确定枢纽基因。使用表达值、随机森林、自适应增强和朴素贝叶斯对生成的转录组学数据进行分类，标签并通过交叉验证将其分为 80/20 的训练和测试数据。确定 ROC 曲线、混淆矩阵和 AUC。此外，还进行了差异表达基因分析的功能富集分析。
随机森林、自适应增强和朴素贝叶斯模型的 AUC 分别为 99%、100%和 75%。与 RF、AdaBoost 和 NB 分类模型相比，AdaBoost 具有最高的 AUC。分类算法可能比重要的生物标志物更好地进行预测。
机器学习模型可从具有牙周炎和急性心肌梗死的基因组数据中预测枢纽基因和非枢纽基因。
