A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph

Table 6 Cociter analysis of top 30 breast cancer driver genes identified by our method

Genes	Driver	Breast	Cancer	Is_driver	Ours	Diffusion	Muf_max	Muf_sum	IntDriver	Driver Net	Dawn Rank
TP53	110	1356	6772	1	1	255	2	1	2	1	2
PIK3CA	54	334	1199	1	2	170	336	6	1	2	1
UBC	2	30	134	0	3	263	224	2	NA	3	128
TTN	2	1	10	0	4	3470	554	19	3	6	104
EP300	4	86	269	1	5	73	135	4	NA	5	175
CDH1	19	358	1410	1	6	39	462	64	5	4	8
PIK3R1	7	21	131	1	7	174	354	30	NA	9	24
GATA3	8	122	154	1	8	91	382	2687	4	8	4
CREBBP	2	41	117	0	9	53	9	44	NA	7	NA
RHOA	6	100	334	0	10	213	693	141	NA	12	199
MAP3K1	2	62	135	1	11	136	1363	184	7	20	3
BRCA1	22	4017	4652	1	12	24	121	144	NA	11	NA
ERBB2	78	4332	5335	1	13	77	10	54	NA	62	79
NCOR1	3	45	109	1	14	151	31	539	30	15	52
SIN3A	3	12	49	0	15	220	93	541	NA	17	NA
ERBB3	4	178	354	1	16	78	407	139	76	27	6
HDAC1	4	99	427	0	17	104	24	128	NA	23	NA
MUC16	1	20	338	1	18	654	7281	1885	6	28	905
DMD	3	2	23	0	19	63	567	114	24	70	5
PTEN	64	672	3047	1	20	203	3	11	41	118	262
ACTB	3	14	61	0	21	2	1244	82	NA	14	131
RB1	10	124	689	1	22	210	1	5	NA	58	35
ATM	5	408	1377	0	23	17	42	423	157	45	NA
ERBB4	4	220	350	1	24	79	468	117	NA	53	217
STAT3	27	332	1824	0	25	242	43	51	NA	31	185
DYNC1H1	2	2	9	0	26	66	1395	654	53	10	17
MYC	45	383	1978	0	27	149	40	46	NA	197	NA
SP1	3	108	393	1	28	231	83	21	NA	67	NA
NEB	1	1	4	0	29	1151	1037	2188	31	400	1162
PLCG2	1	2	13	0	30	181	439	121	NA	49	NA

The first to the fourth column show the co-appeared counts of top 30 identified genes with ‘driver’, ‘breast’ and ‘cancer’ (from the left to the right). Is_driver indicates whether the given gene is a driver or not. The left columns represent the rank positions of identified genes in Subdyquency, Diffusion, Muf_max, Muf_sum, IntDriver, DriverNet and DawnRank respectively

ISSN: 1471-2105