Table 1.

Synthetic response data, predictive errors from held-out test data

	Simulation 1		Simulation 2		Simulation 3
	MA	MAP	MA	MAP	MA	MAP
BVS: EB prior†	0.819±0.004	0.850±0.004	0.837±0.004	0.889±0.005	0.899±0.002	0.918±0.002
BVS: flat prior†	0.845±0.004	0.919±0.005	0.845±0.004	0.919±0.006	0.904±0.002	0.927±0.003
BVS: ‘incorrect’ prior†	0.858±0.003	0.895±0.003	0.918±0.003	1.003±0.004	0.969±0.003	1.036±0.003
BVS: MRF prior†	0.830±0.004	0.877±0.005	0.871±0.004	0.920±0.006	0.886±0.002	0.911±0.002
Lasso†	0.791±0.003		0.790±0.003		0.913±0.002
Li&Li	1.246±0.009		1.476±0.012		1.760±0.012
Baseline linear	1.000±0.002		1.000±0.002		1.000±0.002

Predictions using small-sample training data (n = 35) and held-out test data (n = 818; total of 5,000 train/test pairs) for Simulations 1, 2 and 3. 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. For BVS, predictions made using the posterior predictive distribution with exact model averaging (‘MA’) and using the maximum a posteriori model (‘MAP’).

^‡ linear model with interaction terms for Simulations 1 and 2, and without interaction terms for Simulation 3.