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.
Keywords: AIC; LASSO; biomarkers; feature selection; logistic regression; mass spectrometry.
© 2021 John Wiley & Sons, Ltd.