Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms (2024)
- Authors:
- USP affiliated authors: BLANCO, KATE CRISTINA - IFSC ; BAGNATO, VANDERLEI SALVADOR - IFSC ; PATIÑO, CLAUDIA PATRICIA BARRERA - IFSC ; SOARES, JENNIFER MACHADO - IFSC
- Unidade: IFSC
- DOI: 10.3390/antibiotics13090821
- Subjects: APRENDIZADO COMPUTACIONAL; RESISTÊNCIA MICROBIANA ÀS DROGAS; STREPTOCOCCUS MUTANS; STREPTOCOCCUS PYOGENES; ESCHERICHIA COLI
- Keywords: Antibiotic-resistant bacteria; Machine learning algorithms; Streptococcus pyogenes; Streptococcus mutans; Escherichia coli; Klebsiella pneumoniae
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Antibiotics
- ISSN: 2079-6382
- Volume/Número/Paginação/Ano: v. 13, n. 9, p. 821-1-821-21 + supplementary materials, Sept. 2024
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
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: 29 dez. 2025. -
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/antibiotics13090821 -
NLM
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 2025 dez. 29 ] Available from: https://doi.org/10.3390/antibiotics13090821 -
Vancouver
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 2025 dez. 29 ] Available from: https://doi.org/10.3390/antibiotics13090821 - FTIR-derived feature insights for predicting time-dependent antibiotic resistance progression
- Time evolution of bacterial resistance observed with principal component analysis
- Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification
- Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms
- Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning
- Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm
- Photodynamic inactivation and its effects on the heterogeneity of bacterial resistance
- Combinação de antibiótico com inativação fotodinâmica para o tratamento de infecções bacterianas
- Combinação de antibiótico com inativação fotodinâmica para o tratamento de infecções bacterianas
- Influence of photodynamic action on pure and mixed cultures of gram-negative bacteria: related to growth mechanisms
Informações sobre o DOI: 10.3390/antibiotics13090821 (Fonte: oaDOI API)
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