Before revision:  In this paper, we use the K-means clustering approach for scalable genomic data analysis aiming towards clustering genotypic variants at the population scale. Finally, we train a deep belief network (DBN) for predicting the geographic ethnicity . We used the genotype data from the 1000 Genomes Project, which covers the result of genome sequencing for 2504 individuals from 26 different ethnic origins and comprises 84 million variants.
After revision:  In this paper, we proposed convolutional embedded networks in which we combine two DNN architectures called convolutional embedded clustering and convolutional autoencoder classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning on 95 million GVs from the 1000 Genomes Project, which covers the result of genome sequencing for 2504 individuals from 26 different ethnic origins and comprises 84 million variants.