Inferring genetic interactions via a nonlinear model and an optimization algorithm

BMC Systems Biology

Table 1 Performances of GASA, TSNI, NCA, GAGA and GA-regular SA applied to one repeat of data simulated from Eq. (2) with no noise.

		# int^a	# pc^b	TPR^c	TNR^d	FPR^d	mFPR^e
GASA	AIC/no power law			0.81	0.99	0.01	0.05
	BIC/power law			0.77	0.99	0.01	0.05
GA-regular SA	AIC/no power law			0.69	0.97	0.03	0.18
	BIC/power law			0.73	0.97	0.03	0.14
NCA	100% true connectivity			0.62	0.95	0.05	0.27
	50% true connectivity			0.24	0.85	0.15	0.75
TSNI	inputting prior knowledge: 26 true links	3	1	0.50	0.89	0.11	0.50
		3	2	0.50	0.89	0.11	0.50
		3	3	0.50	0.89	0.11	0.50
GA-GA	AIC/no power law			0.46	0.79	0.21	0.68
	BIC/power law			0.35	0.83	0.17	0.69

^a '# int' denotes the number of interpolations.
^b '# PC' denotes the number of principal components
^c TPR is the ratio of the correctly predicted links to the total number of existing links in a simulated network. Note signs of interactions were not accounted toward TPR and other performance measures.
^d TNR (FPR) is the ratio of correctly predicted non-existing links (false positives) over the total true negatives.
^e mFPR is the ratio of incorrectly predicted links to the total predicted links.

ISSN: 1752-0509