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Recurrent Deep Embedding Networks for Genotype Clustering and Ethnicity Prediction


Md Karim, Michael Cochez, Oya Deniz Beyan, Achille Zappa, Ratnesh Sahay, Stefan Decker, Dietrich-Rebholz Schuhmann

Publication Type: 
Refereed Original Article
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 (ie 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.
Digital Object Identifer (DOI): 
Publication Status: 
Date Accepted for Publication: 
Wednesday, 30 May, 2018
Publication Date: 
CoRR Cornell University
arXiv preprint arXiv:1805.12218
Research Group: 
National University of Ireland, Galway (NUIG)
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