University of Oulu

Tirthankar Paul, Seppo Vainio, Juha Roning, Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network, Expert Systems with Applications, Volume 194, 2022, 116559, ISSN 0957-4174,

Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network

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Author: Paul, Tirthankar1; Vainio, Seppo2; Röning, Juha1
Organizations: 1InfoTech Oulu, Faculty of Information Technology and Electrical Engineering, Biomimetics and Intelligent Systems Group (BISG), University of Oulu, Oulu, Finland
2Infotech Oulu and Kvantum Institute, Faculty of Biochemistry and Molecular Medicine, Disease Networks, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 6.4 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2022-05-20


In this study, chaos game representation (CGR) is introduced for investigating the pattern of genome sequences. It is an image representation of the genome for the overall visualization of the sequence. The CGR representation is a mapping technique that assigns each sequence base into the respective position in the two-dimension plane to portray the DNA sequence. Importantly, CGR provides one to one mapping to nucleotides as well as sequence. A coordinate of the CGR plane can tell the corresponding base and its location in the original genome. Therefore, the whole nucleotide sequence (until the current nucleotide) can be restored from the one point of the CGR. In this study, CGR coupled with artificial neural network (ANN) is introduced as a new way to represent the genome and to classify intra-coronavirus sequences. A hierarchy clustering study is done to validate the approach and found to be more than 90% accurate while comparing the result with the phylogenetic tree of the corresponding genomes. Interestingly, the method makes the genome sequence significantly shorter (more than 99% compressed) saving the data space while preserving the genome features.

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Series: Expert systems with applications
ISSN: 0957-4174
ISSN-E: 1873-6793
ISSN-L: 0957-4174
Volume: 194
Article number: 116559
DOI: 10.1016/j.eswa.2022.116559
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
1183 Plant biology, microbiology, virology
Funding: This work is supported by Infotech Oulu doctoral program.
Copyright information: © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (