用机器学习算法进行唾液分子光谱检测,用于对非完善氨基生殖的诊断分组
Felipe Morando Avelar1, Célia Regina Moreira Lanza2, Sttephany Silva Bernardino3,4
1Department of Genetics, Ecology, and Evolution, ICB, Federal University of Minas Gerais, Belo Horizonte 312-901, MG, Brazil.
International journal of molecular sciences
|September 14, 2024
概括
遗传性质缺陷的非完美化 (AI) 可以通过使用ATR-FTIR光谱和机器学习分析的唾液振动模式来检测. 这种非侵入性方法对精确的AI查有很大的希望.
科学领域:
- 生物化学 生物化学
- 遗传学 遗传学 是一个
- 医学诊断 医学诊断 医学诊断
背景情况:
- 不完美骨髓生成 (AI) 是一种影响牙质形成的遗传疾病,通常是由特定基因的突变引起的.
- 人工智能的复杂表型和遗传基础使得诊断具有挑战性,需要先进的诊断工具.
- 目前用于AI的诊断方法可能是侵入性的和昂贵的,突出了需要可访问的查平台.
研究的目的:
- 为了评估减弱总反射的功效,富里埃变换红外光谱学 (ATR-FTIR) 与机器学习算法相结合,用于区分人工智能患者和健康对照.
- 确定特定的唾液振动模式,可以作为AI检测的潜在生物标志物.
主要方法:
- 通过使用AI患者的唾液样本和匹配的对照进行了一项病例控制试点研究.
- 使用ATR-FTIR光谱分析了唾液振动模式.
- 机器学习算法,包括线性判别分析 (LDA),随机森林和支持向量机 (SVM),用于数据分析和分类.
主要成果:
- 支持矢量机 (SVM) 算法在区分人工智能对象方面表现出最高的性能,达到100%的灵敏度,79%的特异性和88%的准确性.
- 沙普利添加式解释 (SHAP) 确定了五个关键的振动模式 (1010,1013,1002,1004和1011 cm-1),作为AI检测的重要特征.
- 这些发现表明特定的光谱区域作为AI查的潜在唾液生物标志物.
结论:
- 与机器学习相结合的ATR-FTIR光谱学提供了一种非侵入性和准确的方法,用于从对照对象中区分Amelogenesis imperfecta.
- 已确定的唾液振动模式代表了一个有希望的,预先验证的AI查的光谱区域.
- 这种方法具有开发低成本,可访问的AI诊断平台的潜力.
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