Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning
More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.
Authors
- Gintsburg Al (ORCID: https://orcid.org/0000-0003-1769-5059)
- Denis Y. Logunov (ORCID: https://orcid.org/0000-0003-4035-6581)
- M. N. Asatryan (ORCID: https://orcid.org/0000-0001-6273-8615)
- Boris I. Timofeev (ORCID: https://orcid.org/0000-0001-7425-0457)
- I. S. Shmyr (ORCID: https://orcid.org/0000-0002-8514-5174)
- Т. А. Тимофеева (ORCID: https://orcid.org/0000-0002-8991-8525)
- Д. Н. Щербинин (ORCID: https://orcid.org/0000-0002-8518-1669)
- Т. А. Семененко (ORCID: https://orcid.org/0000-0002-6686-9011)
- Ivan F. Ershov (ORCID: https://orcid.org/0000-0002-3333-5347)
- Elita R. Gerasimuk (ORCID: https://orcid.org/0000-0002-7364-163X)
- Anna Nozdracheva (ORCID: https://orcid.org/0000-0002-8521-1741)
- Vaagn G. Agasaryan (ORCID: https://orcid.org/0009-0009-3824-7061)
Institutions
- Dubna State University (RU)
Publication Details
- Journal
- Problems of Virology
- Published
- 2024-09-26
- DOI
- https://doi.org/10.36233/0507-4088-250
- Citations
- 1
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.39