Working PaperArticleVersion 3This version is not peer-reviewed
Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection
Version 1
: Received: 23 April 2020 / Approved: 24 April 2020 / Online: 24 April 2020 (09:21:20 CEST)
Version 2
: Received: 16 June 2020 / Approved: 17 June 2020 / Online: 17 June 2020 (13:26:51 CEST)
Version 3
: Received: 6 December 2020 / Approved: 8 December 2020 / Online: 8 December 2020 (10:17:05 CET)
How to cite:
Taguchi, Y.-H.; Turki, T. Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection. Preprints2020, 2020040431
Taguchi, Y.-H.; Turki, T. Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection. Preprints 2020, 2020040431
Taguchi, Y.-H.; Turki, T. Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection. Preprints2020, 2020040431
APA Style
Taguchi, Y. H., & Turki, T. (2020). Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection. Preprints. https://doi.org/
Chicago/Turabian Style
Taguchi, Y. and Turki Turki. 2020 "Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection" Preprints. https://doi.org/
Abstract
To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.
Keywords
COVID-19; SARS-CoV-2; in silico drug discovery; gene expression profile; tensor decomposition; feature extraction
Subject
Biology and Life Sciences, Anatomy and Physiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Y-H. Taguchi
Commenter's Conflict of Interests: Author