Combining strong sparsity and competitive predictive power with the L‑sOPLS approach for biomarker discovery in metabolomics

Feraud, Baptiste; Munaut, Carine; Manon, Martin; Verleysen, Michel; Govaerts, Bernadette

doi:10.1007/s11306-017-1275-y

Article (Scientific journals)

Combining strong sparsity and competitive predictive power with the L‑sOPLS approach for biomarker discovery in metabolomics

Feraud, Baptiste; Munaut, Carine; Manon, Martin et al.

2017 • In Metabolomics, (13), p. 130

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/227888

DOI
10.1007/s11306-017-1275-y

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Keywords :

Biomarker discovery; (O)PLS models; Feature selection; Sparse models; L-sOPLS; 1H-NMR data; RT-qPCR data

Abstract :

[en] In the context of metabolomics analyses, partial least squares (PLS) represents the standard tool to perform regression and classification. OPLS, the Orthogonal extension of PLS which has proved to be very useful when interpretation is the main issue, is a more recent way to decompose the PLS solution into predictive components correlated to the target Y and components pertaining to the data X but uncorrelated to Y. This predominance of (O)PLS can raise the question of the awareness of alternative multivariate regression and/or classification tools able to find biomarkers. Actually, the search for biomarkers remains a key issue in metabolomics as it is crucial to very accurately target discriminating features.

Disciplines :

Biochemistry, biophysics & molecular biology

Author, co-author :

Feraud, Baptiste

Munaut, Carine ; Université de Liège - ULiège > Département des sciences cliniques > Labo de biologie des tumeurs et du développement

Manon, Martin

Verleysen, Michel

Govaerts, Bernadette

Language :

English

Title :

Combining strong sparsity and competitive predictive power with the L‑sOPLS approach for biomarker discovery in metabolomics

Publication date :

27 September 2017

Journal title :

Metabolomics

ISSN :

1573-3882

eISSN :

1573-3890

Publisher :

Springer, Germany

Issue :

Pages :

130

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 19 September 2018

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