Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

Table 2 Summary of the assessment result for different network construction methods on the time-series gene expression data

Method	Data used	Network size	p-value of chi sq test^a	TPR (%)^b	# mis-class.^c	TP	O/E^d
iBMA-prior	Gene expression + external data	21951	<1.00E-320	18.00	19282	593	4.11
iBMA-shortlist	Gene expression + external data	67440	<1.00E-320	12.78	24673	1287	2.92
Network A from Yeung et al.	Gene expression + external data	65122	1.68E-111	9.98	22485	662	2.28
LASSO-shortlist	Gene expression + external data	255293	<1.00E-320	11.07	46482	4169	2.53
LAR-shortlist	Gene expression + external data	242495	<1.00E-320	11.28	44765	4017	2.57
iBMA-size	Gene expression data only	17202	5.75E-56	16.84	17622	114	3.84
iBMA-noprior	Gene expression data only	63026	1.75E-23	8.85	18903	186	2.02
LASSO-noprior	Gene expression data only	564321	2.56E-10	5.20	38399	1231	1.19
LAR-noprior	Gene expression data only	194687	1.38E-40	7.71	22777	511	1.76

^a The p-value of Pearson’s chi-square test measures the strength of association between an inferred network and the Yeastract database.
^b True positive rate (TPR) is defined as the proportion of inferred regulatory relationships that are documented in Yeastract.
^c The number of misclassified cases is the sum of false positives and false negatives.
^d The O/E ratio is the number of folds the observed number of recovered relationships (i.e., TP) in excess of the expected count of recovery by chance.

ISSN: 1752-0509