BMC Genomic Data

Table 4 Differences between theoretic and estimated penetrance functions (sensitivity analysis: low minor allele frequency)

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	80.29	80.39	303.07^∗	87.65	209.74	249.96
$\sum_{g u^{'}} E_{g u^{'}}$	Environmental model	79.60	278.32	277.18	78.18	170.94	170.94
$\sum_{g u^{'}} E_{g u^{'}}$	Additive model	74.67	369.57	443.10	92.18	303.98	348.50
	Interaction model	180.02	415.60	541.02^∗	191.77	327.44	481.62^∗
	Model 1	113.62	244.87	375.43^∗	179.23	226.03	355.59^∗
$\sum_{g u^{'}} E_{g u^{'}}$	Model 2	232.75	389.70	472.47^∗	318.57	346.57	460.08^∗
$\sum_{g u^{'}} E_{g u^{'}}$	Model 3	253.00	230.12	232.20	256.38	253.67	254.80
	Model 4	133.91	126.27	97.92	138.28	132.11	93.04

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. ^∗Predictions were calculated for all models that do not have unspecified parameters due to empty cells.

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