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

作者：

Highlights：

• CGR representation of SARS-CoV, SARS-CoV2, MERS and Alpha CoV sequences.

• A combine performance of CGR and ANN to detect the virus sequences.

• Encoding the genome sequence into an organized, well represented small data.

• CGR transforms the genome sequence into short even less than 1% of actual size.

摘要

•CGR representation of SARS-CoV, SARS-CoV2, MERS and Alpha CoV sequences.•A combine performance of CGR and ANN to detect the virus sequences.•Encoding the genome sequence into an organized, well represented small data.•CGR transforms the genome sequence into short even less than 1% of actual size.

论文关键词：Chaos game representation,Artificial neural network,Coronavirus

论文评审过程：Received 20 May 2021, Revised 29 December 2021, Accepted 16 January 2022, Available online 21 January 2022, Version of Record 29 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116559