[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
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