Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors

BMC Bioinformatics

Table 8 Expected errors of different Bayesian classification rules in the mixture model for the mammalian cell-cycle network. Expected true error (left) and expected error on unlabeled training data (right), with c ₀=0.6

Method/ n	30	60	90	120	150	Method/ n	30	60	90	120	150
PDCOTP	0.3216	0.3246	0.3280	0.3309	0.3334	PDCOTP	0.3236	0.3270	0.3314	0.3355	0.3339
Jeffreys’	0.4709	0.4743	0.4704	0.4675	0.4654	Jeffreys’	0.4751	0.4621	0.4681	0.4700	0.4645
RMEP	0.3417	0.3340	0.3307	0.3300	0.3299	RMEP	0.3447	0.3409	0.3366	0.3323	0.3316
RMDIP	0.3408	0.3336	0.3300	0.3305	0.3301	RMDIP	0.3442	0.3404	0.3342	0.3344	0.3343
REMLP	0.3754	0.3835	0.3882	0.3857	0.3844	REMLP	0.3748	0.3821	0.3908	0.3826	0.3812
MKDIP-E	0.3411	0.3341	0.3297	0.3297	0.3306	MKDIP-E	0.3457	0.3386	0.3351	0.3312	0.3320
MKDIP-D	0.3407	0.3330	0.3306	0.3304	0.3303	MKDIP-D	0.3482	0.3387	0.3381	0.3342	0.3334
MKDIP-R	0.3457	0.3342	0.3299	0.3286	0.3289	MKDIP-R	0.3449	0.3343	0.3330	0.3306	0.3275

The lowest error for each sample size and the lowest error among practical methods is written in bold

ISSN: 1471-2105