A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Table 1 Comparison of the networks – undirected graphs – produced by three different approaches: the LP-based method proposed here, and techniques proposed by the top two teams of the DREAM2 competition (Challenge 4).

Dataset	Team	Precision at k^thcorrect prediction				Area Under PR Curve	Area Under ROC Curve
		k = 1	k = 2	k = 5	k = 20
IN SILICO 1	Team 70	1.000000	1.000000	1.000000	1.000000	0.596721	0.829266
	Team 80	0.142857	0.181818	0.045045	0.059524	0.070330	0.459704
	LP-SLGN	0.083333	0.086957	0.089286	0.117647	0.087302	0.509624
IN SILICO 2	Team 80	0.333333	0.074074	0.102041	0.069204	0.080266	0.536187
	Team 70	0.142857	0.250000	0.121320	0.081528	0.084303	0.511436
	LP-SLGN	1.000000	1.000000	0.192308	0.183486	0.200265	0.750921
IN SILICO 3	LP-SLGN	0.068966	0.068966	0.068966	0.068966	0.068966	0.500000

For the first k predictions (ranked by score, and for predictions with the same score, taken in the order they were submitted in the prediction files), the DREAM2 evaluation script defines precision as the fraction of correct predictions of k, and recall as the proportion of correct predictions out of all the possible true connections. The other metrics are the Precision-Recall (PR) and Receiver Operating Characteristics (ROC) curves.

ISSN: 1748-7188