Genomic evolution of SARS-CoV-2 delta variants pre- and post-omicron emergence using alignment-free machine learning models.
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| Title: | Genomic evolution of SARS-CoV-2 delta variants pre- and post-omicron emergence using alignment-free machine learning models. |
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| Authors: | Sankar S; Department of Microbiology, Center for Infectious Diseases, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India., Anandharaman K; Department of Artificial Intelligence and Machine Learning, Saveetha Engineering College (Affiliated to Anna University), Chennai, India., Selvam P; Department of Artificial Intelligence and Machine Learning, Saveetha Engineering College (Affiliated to Anna University), Chennai, India., Jayaraman A; Department of Artificial Intelligence and Machine Learning, Saveetha Engineering College (Affiliated to Anna University), Chennai, India., Jayakumar D; State Public Health Laboratory, Directorate of Public Health and Preventive Medicine, DMS Campus, Teynampet, Chennai, Tamil Nadu, India., Balakrishnan P; Centre for Global Health Research, Meenakshi Academy of Higher Education and Research (MAHER), Chennai, India., Larsson M; Division of Molecular Medicine and Virology, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden., Velu V; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Division of Microbiology and Immunology, Emory National Primate Research Center, Emory Vaccine Center, Atlanta, Georgia, United States of America., Raju S; Centre for Global Health Research, Meenakshi Academy of Higher Education and Research (MAHER), Chennai, India., Shankar EM; Infection and Inflammation, Department of Biotechnology, Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, India. |
| Source: | PloS one [PLoS One] 2026 Mar 19; Vol. 21 (3), pp. e0345259. Date of Electronic Publication: 2026 Mar 19 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0345259 |
