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

BMC Systems Biology

Table 4 Results of the different inference methods on DREAM4 networks, challenge size 100 multifactorial

Method	Network (AUPR/AUROC respectively)										Overall
Method	1		2		3		4		5		Overall
Experimental results
ENNET	0.184	0.731	0.261	0.807	0.289	0.813	0.291	0.822	0.286	0.829	52.839
ADANET	0.149	0.664	0.094	0.605	0.191	0.703	0.172	0.712	0.182	0.694	24.970
GENIE3	0.158	0.747	0.154	0.726	0.232	0.777	0.210	0.795	0.204	0.792	37.669
C3NET	0.077	0.562	0.095	0.588	0.126	0.621	0.113	0.687	0.110	0.607	15.015
CLR	0.142	0.695	0.118	0.700	0.178	0.746	0.174	0.748	0.174	0.722	28.806
MRNET	0.138	0.679	0.128	0.698	0.204	0.755	0.178	0.748	0.187	0.725	30.259
ARACNE	0.123	0.606	0.102	0.603	0.192	0.686	0.159	0.713	0.166	0.659	22.744
Winner of the challenge
GENIE3	0.154	0.745	0.155	0.733	0.231	0.775	0.208	0.791	0.197	0.798	37.428
2nd	0.108	0.739	0.147	0.694	0.185	0.748	0.161	0.736	0.111	0.745	28.165
3rd	0.140	0.658	0.098	0.626	0.215	0.717	0.201	0.693	0.194	0.719	27.053

Results of the different inference methods on DREAM4 networks, challenge size 100 multifactorial. 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 competition” part of the table correspond to the best methods participating in the challenge.

ISSN: 1752-0509