Hundreds of compounds are detected during untargeted lipidomics analysis. The potential efficacy of lipids as disease markers makes it important to select the species with the most discriminative potential. Datasets based on a selected class of lipids allow the development of a high‐quality diagnostic model using orthogonal projection on latent structure. The combination of selection of lipids by variable importance in projection and by Akaike information criteria makes it possible to build a reliable diagnostic model based on logistic regression.

中文翻译：

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比较变量选择方法创建用于质谱脂质组分析的生物标志物诊断组的有效性

在非靶向脂质组学分析中检测到数百种化合物。脂质作为疾病标志物的潜在功效使得选择具有最大区别潜力的物种变得很重要。基于选定类别的脂质的数据集允许使用潜在结构上的正交投影开发高质量的诊断模型。通过投影中的可变重要性和 Akaike 信息标准选择脂质的组合使得建立基于逻辑回归的可靠诊断模型成为可能。