title: Incorporating pathway information into boosting estimation of high-dimensional risk prediction models.
abstract: BACKGROUND
  There are several techniques for fitting risk prediction models to high-dimensional data, arising from microarrays. However, the biological knowledge about relations between genes is only rarely taken into account. One recent approach incorporates pathway information, available, e.g., from the KEGG database, by augmenting the penalty term in Lasso estimation for continuous response models.
  
  
  RESULTS
  As an alternative, we extend componentwise likelihood-based boosting techniques for incorporating pathway information into a larger number of model classes, such as generalized linear models and the Cox proportional hazards model for time-to-event data. In contrast to Lasso-like approaches, no further assumptions for explicitly specifying the penalty structure are needed, as pathway information is incorporated by adapting the penalties for single microarray features in the course of the boosting steps. This is shown to result in improved prediction performance when the coefficients of connected genes have opposite sign. The properties of the fitted models resulting from this approach are then investigated in two application examples with microarray survival data.
  
  
  CONCLUSIONS
  The proposed approach results not only in improved prediction performance but also in structurally different model fits. Incorporating pathway information in the suggested way is therefore seen to be beneficial in several ways.
authors.affiliation: Department of Medical Biometry and Statistics, University Medical Center Freiburg, Freiburg, Germany. binderh@fdm.uni-freiburg.de
authors.forename: Harald
authors.initials: H
authors.lastname: Binder
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016016:Proportional Hazards Models; D012044:Regression Analysis