• Corpus ID: 235352993

Automated calibration for stability selection in penalised regression and graphical models: a multi-OMICs network application exploring the molecular response to tobacco smoking

@inproceedings{Bodinier2021AutomatedCF,
  title={Automated calibration for stability selection in penalised regression and graphical models: a multi-OMICs network application exploring the molecular response to tobacco smoking},
  author={Barbara Bodinier and Sarah Filippi and Therese Haugdahl N{\o}st and Julien Chiquet and Marc Chadeau-Hyam},
  year={2021}
}
Stability 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 (LASSO) penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the… 
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