Comparison of the effectiveness of variable selection method for creating a diagnostic panel of biomarkers for mass spectrometric lipidome analysis

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 diagnost...

Full description

Saved in:
Bibliographic Details
Published inJournal of mass spectrometry. Vol. 56; no. 3; pp. e4702 - n/a
Main Authors Tokareva, Alisa O., Chagovets, Vitaliy V., Kononikhin, Alexey S., Starodubtseva, Natalia L., Nikolaev, Evgeny N., Frankevich, Vladimir E.
Format Journal Article
LanguageEnglish
Published England Wiley Subscription Services, Inc 01.03.2021
Subjects
AIC
Biomarkers
Diagnostic systems
feature selection
LASSO
Lipids
logistic regression
mass spectrometry
Regression analysis
Regression models
Spectrometry
mass spectrometry
AIC
logistic regression
biomarkers
feature selection
LASSO
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1076-5174
1096-9888
DOI:10.1002/jms.4702