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基于机器学习的牙周炎的转录诊断

Ya'nan Cheng1, Haiqiong Yang1, Hui Mo1

  • 1Department of Oral Implantation, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, Hainan Province, China.

International dental journal
|November 20, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型使用从转录基因数据中识别的五个关键基因生物标志物准确诊断牙周炎. 这种方法为早期检测和个性化治疗牙疾病提供了更客观,更有效的方法.

关键词:
人工智能的人工智能是人工智能.诊断模型是一种诊断模型.机器学习是机器学习.牙周病是指牙周病的发生.精准医学是一门精准的医学.统计局 统计局 统计局 统计局

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科学领域:

  • 基因组学和生物信息学
  • 计算生物学 计算生物学
  • 翻译医学是一种翻译医学.

背景情况:

  • 牙周炎的诊断依赖于主观和低灵敏度的传统方法,这构成了全球卫生挑战.
  • 转录数据为开发更准确,更有效的诊断工具提供了潜在的途径.

研究的目的:

  • 开发和验证基于机器学习 (ML) 的牙周炎诊断框架,使用转录组数据.
  • 识别新型基因生物标志物,了解它们在牙周炎病原发生中的作用.

主要方法:

  • 来自616个样本 (牙周炎和健康对照) 的转录组数据集的分析.
  • 鉴定差异表达基因 (DEGs),功能丰富,WGCNA和免疫透概况.
  • 使用Boruta和LASSO精制生物标志物,然后进行ML模型构建和验证 (RF,XGBoost).

主要成果:

  • 在培训和验证数据集中,五种诊断生物标志物 (CSF2RB,COL15A1,MME,NEFM,CYP24A1) 已被识别出,其准确度很高.
  • 机器学习模型,特别是随机森林和XGBoost,表现出很好的分类性能.
  • 生物标志物和免疫细胞群体之间观察到显著的相关性,并确定了关键的调节性转录因子 (NFYA,SP1).

结论:

  • 为牙周炎建立了一个基于ML的诊断框架,集成了转录组,免疫和监管网络的洞察力.
  • 鉴定的基因生物标志物和诊断模型显示出在个性化诊断,有针对性的干预和治疗开发中具有临床实用性的潜力.