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dc.contributor.authorBodinier, Barbara
dc.contributor.authorFilippi, Sarah
dc.contributor.authorNøst, Therese Haugdahl
dc.contributor.authorChiquet, Julien
dc.contributor.authorChadeau-Hyam, Marc
dc.description.abstractStability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.en_US
dc.identifier.citationBodinier, Filippi, Nøst, Chiquet, Chadeau-Hyam. Automated calibration for stability selection in penalised regression and graphical models. The Journal of the Royal Statistical Society, Series C (Applied Statistics). 2023;72(5):1375-1393en_US
dc.identifier.cristinIDFRIDAID 2241824
dc.publisherOxford University Pressen_US
dc.relation.journalThe Journal of the Royal Statistical Society, Series C (Applied Statistics)
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/874627/Netherlands/EXposome Powered tools for healthy living in urbAN SEttings/EXPANCE/en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/874739/Finland/Dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases/LONGITOOLS/en_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleAutomated calibration for stability selection in penalised regression and graphical modelsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US

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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)