title: Better diagnostic signatures from RNAseq data through use of auxiliary co-data.
abstract: CONCLUSIONS
  Our aim is to improve omics based prediction and feature selection using multiple sources of auxiliary information: co-data. Adaptive group regularized ridge regression (GRridge) was proposed to achieve this by estimating additional group-based penalty parameters through an empirical Bayes method at a low computational cost. We illustrate the GRridge method and software on RNA sequencing datasets. The method boosts the performance of an ordinary ridge regression and outperforms other classifiers. Post-hoc feature selection maintains the predictive ability of the classifier with far fewer markers.
  
  
  METHODS
  GRridge is an R package that includes a vignette. It is freely available at ( https://bioconductor.org/packages/GRridge/ ). All information and R scripts used in this study, including those on retrieval and processing of the co-data, are available from http://github.com/markvdwiel/GRridgeCodata .
  
  
  BACKGROUND
  mark.vdwiel@vumc.nl.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Putri W
authors.initials: PW
authors.lastname: Novianti
authors.affiliation: Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Barbara C
authors.initials: BC
authors.lastname: Snoek
authors.affiliation: Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Saskia M
authors.initials: SM
authors.lastname: Wilting
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.|Department of Mathematics, VU University, Amsterdam, The Netherlands.
authors.forename: Mark A
authors.initials: MA
authors.lastname: van de Wiel
mesh_terms: D001499:Bayes Theorem; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D058977:Molecular Sequence Annotation; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis / Q000235:genetics