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Table 2 Drug response data, predictive errors from cross-validation

From: Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

BVS: EB prior +int 0.84±0.12 1.00±0.16
BVS: flat prior +int 0.86±0.11 1.26±0.17
BVS: ‘incorrect’ prior +int 0.93±0.15 1.22±0.17
BVS: MRF prior +int 0.86±0.11 1.24±0.17
Lasso +int 0.73±0.10
Li&Li 0.96±0.21
Baseline linear 1.00±0.14
  1. Predictions using leave-one-out-cross-validation (see text for details). Results shown are mean absolute predictive errors ± SEM for the following methods: Bayesian variable selection (BVS) with biologically informative pathway-based prior with source and strength parameters set by empirical Bayes, BVS with flat prior, BVS with ‘incorrect’ prior (contradicting empirical Bayes; see text for details), BVS with a Markov random field (MRF) prior, Lasso regression, penalised-likelihood approach proposed by Li and Li[21], and a baseline linear regression without interaction terms including all 11 predictors. ‘+int’ denotes linear model with interaction terms. For BVS, predictions made using the posterior predictive distribution with exact model averaging (‘MA’) and using the maximum a posteriori model (‘MAP’).