A computational model based on long short-term memory for predicting organellar genes in plastid genomes

Jaehee Jung; Gangman Yi

doi:10.1109/BIBM47256.2019.8983030

A computational model based on long short-term memory for predicting organellar genes in plastid genomes

Jaehee Jung
, Gangman Yi

Department of Computer Science and Artificial Intelligence

Myongji University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Gene annotation tools for the identification of gene functions are often based on similarity with reference sequences, such as those of model organisms. If sequence data for a relevant model organism are not available, it is necessary to use data for closely related organisms, but methods for identifying related organisms are computationally intensive. We propose the application of LSTM (long short-term memory) models for the automatic annotation of genes by generating a training model with sequences of same taxonomic group. The proposed method to identify unknown sequences enables annotation without reference sequences.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1200-1202
Number of pages	3
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983030
State	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Gene Annotation
LSTM
Organellar Genes
Reference Sequences

Access to Document

10.1109/BIBM47256.2019.8983030

Cite this

Jung, J., & Yi, G. (2019). A computational model based on long short-term memory for predicting organellar genes in plastid genomes. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1200-1202). Article 8983030 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983030

Jung, Jaehee ; Yi, Gangman. / A computational model based on long short-term memory for predicting organellar genes in plastid genomes. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1200-1202 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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title = "A computational model based on long short-term memory for predicting organellar genes in plastid genomes",

abstract = "Gene annotation tools for the identification of gene functions are often based on similarity with reference sequences, such as those of model organisms. If sequence data for a relevant model organism are not available, it is necessary to use data for closely related organisms, but methods for identifying related organisms are computationally intensive. We propose the application of LSTM (long short-term memory) models for the automatic annotation of genes by generating a training model with sequences of same taxonomic group. The proposed method to identify unknown sequences enables annotation without reference sequences.",

keywords = "Gene Annotation, LSTM, Organellar Genes, Reference Sequences",

author = "Jaehee Jung and Gangman Yi",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

month = nov,

doi = "10.1109/BIBM47256.2019.8983030",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1200--1202",

editor = "Illhoi Yoo and Jinbo Bi and Hu, \{Xiaohua Tony\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

address = "United States",

}

Jung, J & Yi, G 2019, A computational model based on long short-term memory for predicting organellar genes in plastid genomes. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983030, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1200-1202, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983030

A computational model based on long short-term memory for predicting organellar genes in plastid genomes. / Jung, Jaehee; Yi, Gangman.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1200-1202 8983030 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A computational model based on long short-term memory for predicting organellar genes in plastid genomes

AU - Jung, Jaehee

AU - Yi, Gangman

PY - 2019/11

Y1 - 2019/11

N2 - Gene annotation tools for the identification of gene functions are often based on similarity with reference sequences, such as those of model organisms. If sequence data for a relevant model organism are not available, it is necessary to use data for closely related organisms, but methods for identifying related organisms are computationally intensive. We propose the application of LSTM (long short-term memory) models for the automatic annotation of genes by generating a training model with sequences of same taxonomic group. The proposed method to identify unknown sequences enables annotation without reference sequences.

AB - Gene annotation tools for the identification of gene functions are often based on similarity with reference sequences, such as those of model organisms. If sequence data for a relevant model organism are not available, it is necessary to use data for closely related organisms, but methods for identifying related organisms are computationally intensive. We propose the application of LSTM (long short-term memory) models for the automatic annotation of genes by generating a training model with sequences of same taxonomic group. The proposed method to identify unknown sequences enables annotation without reference sequences.

KW - Gene Annotation

KW - LSTM

KW - Organellar Genes

KW - Reference Sequences

UR - https://www.scopus.com/pages/publications/85084333395

U2 - 10.1109/BIBM47256.2019.8983030

DO - 10.1109/BIBM47256.2019.8983030

M3 - Conference contribution

AN - SCOPUS:85084333395

T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

SP - 1200

EP - 1202

BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

A2 - Yoo, Illhoi

A2 - Bi, Jinbo

A2 - Hu, Xiaohua Tony

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Y2 - 18 November 2019 through 21 November 2019

ER -

Jung J, Yi G. A computational model based on long short-term memory for predicting organellar genes in plastid genomes. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1200-1202. 8983030. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983030

A computational model based on long short-term memory for predicting organellar genes in plastid genomes

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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