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Comparison of the effectiveness of variable selection method for creating a diagnostic panel of biomarkers for mass spectrometric lipidome analysis
Author(s) -
Tokareva Alisa O.,
Chagovets Vitaliy V.,
Koikhin Alexey S.,
Starodubtseva Natalia L.,
Nikolaev Evgeny N.,
Frankevich Vladimir E.
Publication year - 2021
Publication title -
journal of mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 1076-5174
DOI - 10.1002/jms.4702
Subject(s) - lipidome , lipidomics , akaike information criterion , discriminative model , chemistry , feature selection , computational biology , selection (genetic algorithm) , chemometrics , logistic regression , chromatography , artificial intelligence , machine learning , computer science , biochemistry , biology
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.