Title: Recurrent Deep Embedding Networks for Genotype Clustering and Ethnicity Prediction,
Abstract: The understanding of variations in genome sequences assists us in identifying
people who are predisposed to common diseases, solving rare diseases, and
finding the corresponding population group of the individuals from a larger
population group. Although classical machine learning techniques allow
researchers to identify groups (i.e. clusters) of related variables, the
accuracy, and effectiveness of these methods diminish for large and
high-dimensional datasets such as the whole human genome. On the other hand,
deep neural network architectures (the core of deep learning) can better
exploit large-scale datasets to build complex models. 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. Our experimental results, with a focus on accuracy and
scalability, show the effectiveness and superiority compared to the
state-of-the-art.