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Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness

hal.structure.identifierUnité Mixte de Recherche sur les Herbivores - UMR 1213 [UMRH]
dc.contributor.authorELLIES-OURY, Marie-Pierre
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierUnité Mixte de Recherche sur les Herbivores - UMR 1213 [UMRH]
dc.contributor.authorCONANEC, Alexandre
hal.structure.identifierUnité Mixte de Recherche sur les Herbivores - UMR 1213 [UMRH]
dc.contributor.authorBONNET, Muriel
hal.structure.identifierUnité Mixte de Recherche sur les Herbivores - UMR 1213 [UMRH]
dc.contributor.authorPICARD, Brigitte
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jerôme
dc.date.accessioned2024-04-04T02:54:30Z
dc.date.available2024-04-04T02:54:30Z
dc.date.issued2019
dc.identifier.issn2045-2322
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192277
dc.description.abstractEnIn this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three regression models (parametric, semi-parametric and non-parametric) are considered and estimated by multiple linear regression, sliced inverse regression and random forests. Both the variables selection and the model choice are computational. A measure of importance based on random perturbations is calculated for each covariate. The variables above a threshold are selected. Then a learning/test samples approach is used to estimate the Mean Square Error and to determine which model (including variable selection) is the most accurate. The R package modvarsel (MODel and VARiable SELection) implements this computational approach and applies to any regression datasets. After checking the good behavior of the methodology on simulated data, the R package is used to select the proteins predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls. The biomarkers were selected by linear regression (the best regression model) to predict meat tenderness. These biomarkers, we confirm the predominant role of heat shock proteins and metabolic ones.
dc.language.isoen
dc.publisherNature Publishing Group
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.enStatistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness
dc.typeArticle de revue
dc.identifier.doi10.1038/s41598-019-46202-y
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halStatistiques [stat]
bordeaux.journalScientific Reports
bordeaux.volume9
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue1
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-02429345
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02429345v1
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