Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

BMC Bioinformatics

Table 2 Pathway enrichment of gene sets under different models (GBM dataset)

K	MWSM	\(r_{pe}(\%)\)
2	CDKN2B CDK4	100.0
3	CDKN2B CDK4 RB1	100.0
4	CDKN2B RB1 TSPAN31 ERBB2	50.0
5	CDKN2B RB1 ERBB2 TSPAN31 PPP2R1A	40.0
6	CDKN2B RB1 CDK4 ERBB2 MSH2 NKG7	50.0
7	CDKN2B RB1 CDK4 DBC1 BCAS1 CD33 ERBB2	42.9
8	ERBB2 CDK4 RELN FGF21 RB1 PRF1 NTRK3 CDKN2B	50.0

K	SMCM	\(r_{pe}(\%)\)
2	CDKN2B CDK4	100.0
3	CDKN2B CDK4 TP53	100.0
4	CDKN2B CDK4 RB1 TP53	100.0
5	FGFR3 NF1 TP53 CDKN2B TSPAN31	60.0
6	NF1 TP53 CDKN2B CYP27B1 DBC1 SYNE1	33.3
7	EGFR TP53 CDK4 RELN TEK CDKN2B NF1	57.1
8	EGFR TP53 FGFR3 RELN NF1 CDKN2B SYNE1 CYP27B1	50.0

K	SMCMN	\(r_{pe}(\%)\)
2	CDKN2B CDK4	100.0
3	CDKN2A CDK4 TP53	100.0
4	CDKN2A CDK4 TP53 RB1	100.0
5	CDKN2A CDK4 TP53 CCNE1 RB1	100.0
6	PIK3CA TP53 PTEN ERBB2 EGFR CDKN2A	83.3
7	PIK3CA TP53 PTEN ERBB2 EGFR PIK3R1 CDKN2A	85.7
8	CDKN2A CDK4 TP53 CASP3 CCNE1 RB1 PIK3CA FOXO1	62.5

Bold indicate that the genes are enriched in the same biological signaling pathway

ISSN: 1471-2105