Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.
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| Title: | Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques. |
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| Authors: | Elsherbini AMA; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt., Elkholy AH; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt., Fadel YM; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt., Goussarov G; Microbiology Unit, Belgian Nuclear Research Centre (SCK•CEN), Mol, Belgium., Elshal AM; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt., El-Hadidi M; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt., Mysara M; Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt. mmaysara@nu.edu.eg. |
| Source: | BMC bioinformatics [BMC Bioinformatics] 2024 Mar 27; Vol. 25 (1), pp. 131. Date of Electronic Publication: 2024 Mar 27. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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