ENNET: inferring large gene regulatory networks from expression data using gradient boosting

BMC Systems Biology

Table 5 Results of the different inference methods on DREAM5 networks

Method	Network (AUPR/AUROC respectively)						Overall
Method	1		3		4		Overall
Experimental results
ENNET	0.432	0.867	0.069	0.642	0.021	0.532	>300
ADANET	0.261	0.725	0.083	0.596	0.021	0.517	16.006
GENIE3	0.291	0.814	0.094	0.619	0.021	0.517	40.335
C3NET	0.080	0.529	0.026	0.506	0.018	0.501	0.000
CLR	0.217	0.666	0.050	0.538	0.019	0.505	4.928
MRNET	0.194	0.668	0.041	0.525	0.018	0.501	2.534
ARACNE	0.099	0.545	0.029	0.512	0.017	0.500	0.000
Winner of the challenge
GENIE3	0.291	0.815	0.093	0.617	0.021	0.518	40.279
ANOVA η²	0.245	0.780	0.119	0.671	0.022	0.519	34.023
TIGRESS	0.301	0.782	0.069	0.595	0.020	0.517	31.099

Results of the different inference methods on DREAM5 networks. An area under the ROC curve (AUROC) and an area under the Precision-Recall curve (AUPR) are given for each network respectively. The Overall Score for all the networks is given in the last column. The best results for each column are in bold. Numbers in the “Experimental results” part of the table were collected after running the algorithms with the default sets of parameters on pre-processed data. Numbers in the “Winner of the challenge” part of the table correspond to the best methods participating in the challenge.

ISSN: 1752-0509