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...
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| Published in | Journal of mass spectrometry. Vol. 56; no. 3; pp. e4702 - n/a |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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| 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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| AbstractList | 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. 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. |
| Author | Chagovets, Vitaliy V. Frankevich, Vladimir E. Nikolaev, Evgeny N. Starodubtseva, Natalia L. Tokareva, Alisa O. Kononikhin, Alexey S. |
| Author_xml | – sequence: 1 givenname: Alisa O. surname: Tokareva fullname: Tokareva, Alisa O. organization: Russian Academy of Sciences – sequence: 2 givenname: Vitaliy V. surname: Chagovets fullname: Chagovets, Vitaliy V. organization: Healthcare of Russian Federation – sequence: 3 givenname: Alexey S. surname: Kononikhin fullname: Kononikhin, Alexey S. organization: Skolkovo Institute of Science and Technology – sequence: 4 givenname: Natalia L. surname: Starodubtseva fullname: Starodubtseva, Natalia L. organization: Healthcare of Russian Federation – sequence: 5 givenname: Evgeny N. surname: Nikolaev fullname: Nikolaev, Evgeny N. organization: Skolkovo Institute of Science and Technology – sequence: 6 givenname: Vladimir E. surname: Frankevich fullname: Frankevich, Vladimir E. email: vfrankevich@gmail.com organization: Healthcare of Russian Federation |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33629457$$D View this record in MEDLINE/PubMed |
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| Title | Comparison of the effectiveness of variable selection method for creating a diagnostic panel of biomarkers for mass spectrometric lipidome analysis |
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