Deep learning approach to biogeographical ancestry inference

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

Biogeographical ancestry (BGA) inference is based on the understanding of genetic diversity distribution among population groups. BGA inference is used to detect and measure the population structure that presents the natural assignment in genetic terms, identify genetic patterns found in individuals' genotypes, and estimate an individual's BGAs. In the context of forensic, BGA inference at an individual level gives the possibilities to achieve more complete identification of missing person or suspect. Current machine learning approach to BGA inference based on Bayesian theory and principle component analysis cannot operate on the data sequence directly and require predefined features extracted from the data sequence based on prior knowledge. In this paper, we conduct a survey of the state of the art of BGA inference and propose a new approach based on deep learning to BGA inference without prior feature extraction to find hidden genetic structure and provide more accurate predictions. Our experiments conducted on the dataset for Human Genome Diversity Project (HGDP) show better results for the proposed approach.

Original languageEnglish
Title of host publication23rd KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems KES2019
EditorsI. J Rudas, C Janos, C. Toro, J. Botzheim, R. J. Howlett, L. C. Jain
Place of PublicationNetherlands
PublisherElsevier
Pages552-561
Number of pages10
Volume159
DOIs
Publication statusPublished - 2019
Event23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019 - Budapest, Hungary
Duration: 4 Sep 20196 Sep 2019

Publication series

NameProcedia Computer Science
PublisherElsevier BV
ISSN (Print)1877-0509

Conference

Conference23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019
CountryHungary
CityBudapest
Period4/09/196/09/19

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