Source: Antibiotics. Unidade: IFSC
Subjects: APRENDIZADO COMPUTACIONAL, RESISTÊNCIA MICROBIANA ÀS DROGAS, STREPTOCOCCUS MUTANS, STREPTOCOCCUS PYOGENES, ESCHERICHIA COLI
ABNT
PATIÑO, Claudia Patricia Barrera et al. Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms. Antibiotics, v. 13, n. 9, p. 821-1-821-21 + supplementary materials, 2024Tradução . . Disponível em: https://doi.org/10.3390/antibiotics13090821. Acesso em: 08 set. 2024.APA
Patiño, C. P. B., Soares, J. M., Blanco, K. C., & Bagnato, V. S. (2024). Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms. Antibiotics, 13( 9), 821-1-821-21 + supplementary materials. doi:10.3390/antibiotics13090821NLM
Patiño CPB, Soares JM, Blanco KC, Bagnato VS. Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms [Internet]. Antibiotics. 2024 ; 13( 9): 821-1-821-21 + supplementary materials.[citado 2024 set. 08 ] Available from: https://doi.org/10.3390/antibiotics13090821Vancouver
Patiño CPB, Soares JM, Blanco KC, Bagnato VS. Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms [Internet]. Antibiotics. 2024 ; 13( 9): 821-1-821-21 + supplementary materials.[citado 2024 set. 08 ] Available from: https://doi.org/10.3390/antibiotics13090821