AI Article Synopsis
- - Hundreds of lipid compounds can be identified in untargeted lipidomics analysis, highlighting their potential as disease markers.
- - To create effective diagnostic models, it's crucial to select the lipids that best differentiate between conditions using methods like orthogonal projection on latent structure.
- - A robust logistic regression diagnostic model can be developed by selecting lipids based on their significance in the analytical process, utilizing both variable importance and Akaike information criteria.
Article Abstract
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.
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| http://dx.doi.org/10.1002/jms.4702 | DOI Listing |
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