Construction of Gene Expression Patterns to Identify Critical Genes under SARS-CoV-2 Infection Conditions.
IEEE/ACM Trans Comput Biol Bioinform
; PP2023 Jun 07.
Article
in English
| MEDLINE | ID: covidwho-20245128
ABSTRACT
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus with an envelope frequently altered by unstable genetic material, making it extremely difficult for vaccines, drugs, and diagnostics to work. Understanding SARS-CoV-2 infection mechanisms requires studying gene expression changes. Deep learning methods are often considered for large-scale gene expression profiling data. Data feature-oriented analysis, however, neglects the biological process nature of gene expression, making it difficult to describe gene expression behaviors accurately. In this paper, we propose a novel scheme for modeling gene expression during SARS-CoV-2 infection as networks (gene expression modes, GEM), to characterize their expression behaviors. On this basis, we investigated the relationships among GEMs to determine SARS-CoV-2's core radiation mode. Our final experiments identified key COVID-19 genes by gene function enrichment, protein interaction, and module mining. Experimental results show that ATG10, ATG14, MAP1LC3B, OPTN, WDR45, and WIPI1 genes contribute to SARS-CoV-2 virus spread by affecting autophagy.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Topics:
Vaccines
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2023
Document Type:
Article
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