华西口腔医学杂志2024，Vol.42Issue(2)：214-226,13.DOI:10.7518/hxkq.2024.2023275
基于随机森林和人工神经网络构建种植体周炎的诊断模型
Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis
摘要
Abstract
Objective This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest(RF)and artificial neural network(ANN).Methods GSE-33774,GSE106090,and GSE57631 datasets were obtained from the GEO database.The GSE33774 and GSE106090 da-tasets were analyzed for differential expression and functional enrichment.The protein-protein interaction networks(PPI)and RF screened vital genes.A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets.A transcription factor-gene interaction network and a transcription factor-micro-RNA(miRNA)regulatory network were also established.Results A total of 124 differentially expressed genes(DEGs)involved in the regulation of peri-implantitis were screened.Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration.The PPI and RF screened six essential genes,namely,CD38,CYBB,FCGR2A,SELL,TLR4,and CXCL8.The receiver oper-ating characteristic curve(ROC)indicated that the ANN model had an excellent diagnostic performance.FOXC1,GA-TA2,and NF-κB1 may be essential transcription factors in peri-implantitis,and hsa-miR-204 may be a key miRNA.Con-clusion The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence,and CD38,CYBB,FCGR2A,SELL,TLR4,and CXCL8 are potential diagnostic markers.FOXC1,GATA2,and NF-κB1 may be essential transcription factors in peri-implantitis,and hsa-miR-204 plays a vital role as a critical miRNA.关键词
种植体周炎/生物信息学/随机森林/人工神经网络/诊断模型Key words
peri-implantitis/bioinformatics/random forest/artificial neural network/diagnostic model分类
医药卫生引用本文复制引用
杨浩然,陈宇翔,赵安娜,程婷婷,周建忠,李自良..基于随机森林和人工神经网络构建种植体周炎的诊断模型[J].华西口腔医学杂志,2024,42(2):214-226,13.基金项目
Yunnan Provincial Health and Family Planning Commission Medical Reserve Talent Program(H20-17054) 云南省卫生和计划生育委员会医学后备人才项目(H-2017054) （H20-17054）
