Learning genetic epistasis using Bayesian network scoring criteria

BMC Bioinformatics

Table 1 Accuracies of scoring criteria

	Scoring Criterion	200	400	800	1600	Total
1	score _α _{= 15}	4379	5426	6105	6614	22524
2	score _α _{= 12}	4438	5421	6070	6590	22519
3	score _α _{= 18}	4227	5389	6095	6625	22336
4	score _α _{= 9}	4419	5349	5996	6546	22313
5	score _α _{= 21}	3989	5286	6060	6602	21934
6	score _α _{= 6}	4220	5165	5874	6442	21701
7	score _MML ₁	4049	5111	5881	6463	21504
8	score _α _{= 24}	3749	5156	5991	6562	21448
9	score _MDR	4112	4954	5555	5982	20603
10	score _α _{= 3}	3839	4814	5629	6277	20559
11	score _Epi ₂	3571	4791	5648	6297	20307
12	score _α _{= 30}	3285	4779	5755	6415	20234
13	score _MML ₂	3768	4914	5754	5780	20216
14	score _Epi ₁	2344	5225	6065	6553	20187
15	score _Suz ₁	3489	4580	5521	6215	19805
16	score _α _{= 36}	2810	4393	5464	6150	18817
17	score _α _{= 42}	2310	4052	5158	5895	17415
18	score _K ₂	1850	3475	5095	6116	16536
19	score _Suz ₂	2245	3529	4684	5673	16131
20	score _α _{= 54}	1651	3297	4492	5329	14769
21	score _AIC ₂	3364	3153	2812	2520	11847
22	score _AIC ₁	2497	1967	1462	1126	7052
23	score _α _{= 162}	26	476	1300	2046	3848

The number of times out of 7000 data sets that each scoring criterion identified the correct model for sample sizes of 200, 400, 800, and 1600. The last column gives the total accuracy over all sample sizes. The scoring criteria are listed in descending order of total accuracy.

ISSN: 1471-2105