BMC Genomic Data

Table 2 Differences between theoretic and estimated penetrance functions (models by Amato et al. [[14]])

From: Artificial neural networks modeling gene-environment interaction

			High risk scenario			Low risk scenario
		Neural network	Logistic regression	Logistic regression (DV)	Neural network	Logistic regression	Logistic regression (DV)
			*n =1000 + 1000*			*n=1000 + 1000*
	Genetic model	40.79	31.31	48.15	48.22	40.85	83.62
$\sum_{g u^{'}} E_{g u^{'}}$	Environmental model	46.14	277.11	277.11	52.45	171.61	171.36
$\sum_{g u^{'}} E_{g u^{'}}$	Additive model	45.13	256.52	260.10	47.99	163.19	189.92
	Interaction model	119.77	345.77	247.93	132.47	225.61	194.37
			*n =500 + 500*			*n = 500 + 500*
	Genetic model	59.28	47.14	68.22	64.27	92.02	159.80
$\sum_{g u^{'}} E_{g u^{'}}$	Environmental model	60.57	277.51	277.15	90.76	174.37	174.16
$\sum_{g u^{'}} E_{g u^{'}}$	Additive model	56.10	268.11	297.62	80.66	190.25	242.34
	Interaction model	138.91	344.50	268.75	153.56	233.16	210.98
			*n = 200 + 200*			*n = 200 + 200*
	Genetic model	101.95	85.67	152.25	97.23	167.48	207.66
$\sum_{g u^{'}} E_{g u^{'}}$	Environmental model	96.32	278.40	278.93	163.16	177.14	175.27
$\sum_{g u^{'}} E_{g u^{'}}$	Additive model	96.16	329.55	374.17	177.24	246.06	292.39
	Interaction model	168.90	349.88	316.01	207.81	256.22	291.88

Sum of mean absolute differences between theoretic and estimated penetrance function for 100 case-control data sets in the low and high risk scenario for different sample sizes. Bold numbers mark the best model fit comparing neural networks and logistic regression models. DV = design variables.

Back to article page

ISSN: 2730-6844

Contact us

General enquiries: journalsubmissions@springernature.com