BMC Genomic Data

Table 3 Differences between theoretic and estimated penetrance functions (models representing a masking effect of the genetic factor)

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
	Model 1	38.63	211.62	105.83	41.07	195.15	87.57
$\sum_{g u^{'}} E_{g u^{'}}$	Model 2	117.94	359.10	155.40	101.92	323.89	114.71
$\sum_{g u^{'}} E_{g u^{'}}$	Model 3	40.67	253.01	85.51	43.15	258.19	65.87
	Model 4	103.37	228.10	85.16	103.63	227.50	59.74
			*n = 500 + 500*			*n = 500 + 500*
	Model 1	54.58	219.39	136.26	70.40	207.97	140.74
$\sum_{g u^{'}} E_{g u^{'}}$	Model 2	144.35	363.36	176.74	183.28	327.58	143.06
$\sum_{g u^{'}} E_{g u^{'}}$	Model 3	60.98	261.86	110.93	66.25	278.61	114.68
	Model 4	143.62	235.44	102.13	115.59	237.14	81.13
			*n = 200 + 200*			*n = 200 + 200*
	Model 1	126.56	252.88	251.70	192.47	244.17	225.63
$\sum_{g u^{'}} E_{g u^{'}}$	Model 2	262.92	371.69	230.25	297.68	348.46	215.70
$\sum_{g u^{'}} E_{g u^{'}}$	Model 3	139.27	324.55	215.12	141.28	328.64	191.61
	Model 4	189.69	287.39	169.86	164.13	280.21	149.95

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