Fig. 3
From: Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods
One dimensional architecture. Selected SNPs are passed to a 1DCNN. N, X, Y, and Z represent the size of the input layer, and X, Y, Z represent the filter size for the first layer, second layer, and third layer. A and B represents the number of the filter in the first layer and second layer. As it is 1DCNN so kernel size or filter size has one dimension equal to 1 and the other is variable. The number of hidden layers, the number of filters in each layer, and the size of the filter can be changed. It is important to form the proper model. At the end output of the last 1DCNN layer, after global averaging, is connected to the fully connected network. In a fully connected network number of layers and the number of neurons in each layer can also be changed [45]