Machine learning-based analysis of Ebola virus' impact on gene expression in nonhuman primates
This study provides valuable insights into EBOV pathogenesis, demonstrating the potential of SMAS to enhance the precision of diagnostics and interventions for Ebola and other viral infections.
Authors
- Mostafa Rezapour (ORCID: https://orcid.org/0000-0001-9569-118X)
- Muhammad Khalid Khan Niazi (ORCID: https://orcid.org/0000-0002-1278-7512)
- Aarthi Narayanan (ORCID: https://orcid.org/0000-0002-4889-4416)
- Metin N. Gürcan (ORCID: https://orcid.org/0000-0002-2421-8229)
- Hao Lü (ORCID: https://orcid.org/0009-0007-6094-458X)
Institutions
- George Mason University (US)
- Wake Forest University (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2024-08-30
- DOI
- https://doi.org/10.3389/frai.2024.1405332
- Citations
- 6
- Primary Topic
- Viral Infections and Outbreaks Research
- Type
- article
- Field-Weighted Citation Impact
- 2.33
Funders
- Defense Threat Reduction Agency