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Authors: Tanzila Islam 1 ; Chyon Kim 1 ; Hiroyoshi Iwata 2 ; Hiroyuki Shimono 3 ; Akio Kimura 1 ; Hein Zaw 4 ; Chitra Raghavan 4 ; Hei Leung 4 and Rakesh Singh 5

Affiliations: 1 Department of Systems Innovation Engineering, Graduate School of Science and Engineering, Iwate University, Morioka, Iwate, Japan ; 2 Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan ; 3 Crop Science Laboratory, Faculty of Agriculture, Iwate University, Morioka, Japan ; 4 International Rice Research Institute (IRRI), Laguna, Philippines ; 5 International Center for Biosaline Agriculture (ICBA), Dubai, U.A.E.

Keyword(s): Genome-wide DNA Polymorphisms, Stacked Autoencoder, Deep Neural Network, Separate Stacking Model, Genome Compression, Missing Value Imputation.

Abstract: Missing value imputation and compressing genome-wide DNA polymorphism data are considered as a challenging task in genomic data analysis. Missing data consists in the lack of information in a dataset that directly influences data analysis performance. The aim is to develop a deep learning model named Autoencoder Genome Imputation and Compression (AGIC) which can impute missing values and compress genome-wide polymorphism data using a separated neural network model to reduce the computational time. This research will challenge the construction of a model by using Autoencoder for genomic analysis, in other words, a fusion research between agriculture and information sciences. Moreover, there is no knowledge of missing value imputation and genome-wide polymorphism data compression using Separated Stacking Autoencoder Model. The main contributions are: (1) missing value imputation of genome-wide polymorphism data, (2) genome-wide polymorphism data compression of Rice DNA. To demonstrate the usage of AGIC model, real genome-wide polymorphism data from a rice MAGIC population has been used. (More)

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Paper citation in several formats:
Islam, T.; Kim, C.; Iwata, H.; Shimono, H.; Kimura, A.; Zaw, H.; Raghavan, C.; Leung, H. and Singh, R. (2021). A Deep Learning Method to Impute Missing Values and Compress Genome-wide Polymorphism Data in Rice. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS, ISBN 978-989-758-490-9; ISSN 2184-4305, pages 101-109. DOI: 10.5220/0010233901010109

@conference{bioinformatics21,
author={Tanzila Islam. and Chyon Kim. and Hiroyoshi Iwata. and Hiroyuki Shimono. and Akio Kimura. and Hein Zaw. and Chitra Raghavan. and Hei Leung. and Rakesh Singh.},
title={A Deep Learning Method to Impute Missing Values and Compress Genome-wide Polymorphism Data in Rice},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS,},
year={2021},
pages={101-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010233901010109},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS,
TI - A Deep Learning Method to Impute Missing Values and Compress Genome-wide Polymorphism Data in Rice
SN - 978-989-758-490-9
IS - 2184-4305
AU - Islam, T.
AU - Kim, C.
AU - Iwata, H.
AU - Shimono, H.
AU - Kimura, A.
AU - Zaw, H.
AU - Raghavan, C.
AU - Leung, H.
AU - Singh, R.
PY - 2021
SP - 101
EP - 109
DO - 10.5220/0010233901010109

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