GBNet: Deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach

Table 1 Sequence constraints learned by BBNet and GBNet in the five simulated datasets

Perc^a	Rank^b	BBNet		GBNet
		BS	Rules	BS	Rules
0.4	2,5	-130.49	1. Distance to TSS of M604:180 2. Presence of PAC 3. Presence of M599	-123.48	1. Distance to TSS of M604:140 2. Distance between RRPE and PAC:40 3. Distance to TSS of M599:160 4. Presence of M593
0.5	1,3	-120.03	1. Presence of PAC 2. Distance to TSS of M604:180 3. Presence of M599	-109.28	1. Distance to TSS of M604:200 2. Distance between RRPE and PAC:40 3. Distance to TSS of M599:480
0.6	1,3	-114.19	1. Presence of PAC 2. Distance to TSS of M604:180 3. Presence of RRPE 4. Presence of M599	-102.11	1. Distance between PAC and RRPE:40 2. Distance to TSS of M604:140 3. Distance between M604 and M599:500
0.7	1,2	-102.68	1. Presence of PAC 2. Presence of RRPE 3. Distance to TSS of M604:140	-91.18	1. Distance between PAC and RRPE:40 2. Distance to TSS of M604:140
0.8	1,2	-85.85	1. Presence of PAC 2. Presence of RRPE 3. Distance to TSS of M604:140	-70.1268	1. Distance between PAC and RRPE:40 2. Distance between M604 and M599:340 3. Distance to TSS of M604:140

The MXXX are AlignACE motif matrices taken from [16].
^aThe percentage of sequences satisfying the spacing rule between PAC and RRPE motifs ranges from 0.4 to 0.8. ^bThe single motif ranks for PAC and RRPE in each dataset are also shown: the first is PAC and the second is RRPE.

ISSN: 1471-2105