New methods for separating causes from effects in genomics data

BMC Genomics

Table 7 AUC of causal orientation

Method	ECOLI	YEAST	NOTCH1	RELA
ANM	0.464	0.379	0.456	0.369
PNL	0.443	0.464	0.520	0.520
IGCI (Uniform/Entropy)	0.713	0.409	0.708	0.805
IGCI (Uniform/Integral)	0.642	0.437	0.631	0.757
IGCI (Gaussian/Entropy)	0.813	0.613	0.935	0.967
IGCI (Gaussian/Integral)	0.724	0.655	0.834	0.927
GPI-MML	0.488	0.370	0.184	0.333
ANM-MML	0.393	0.237	0.078	0.071
GPI	0.536	0.396	0.594	0.513
ANM-GAUSS	0.474	0.476	0.807	0.446
LINGAM	0.462	0.463	0.362	0.392
RANDOM	0.500	0.500	0.500	0.500

For each gold standard (column) dark orange cells correspond to methods that have high values of AUC, while white cells correspond to methods that have low values of AUC. AUCs higher than 0.50 are shown in bold.

ISSN: 1471-2164