Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning (2024)
- Authors:
- USP affiliated authors: BLANCO, KATE CRISTINA - IFSC ; INADA, NATALIA MAYUMI - IFSC ; BAGNATO, VANDERLEI SALVADOR - IFSC ; PATIÑO, CLAUDIA PATRICIA BARRERA - IFSC ; SOARES, JENNIFER MACHADO - IFSC
- Unidade: IFSC
- DOI: 10.1364/LAOP.2024.Tu4A.20
- Subjects: RESISTÊNCIA MICROBIANA ÀS DROGAS; APRENDIZADO COMPUTACIONAL
- Language: Inglês
- Imprenta:
- Publisher: Optica Publishing Group - OPG
- Publisher place: Washington, DC
- Date published: 2024
- Source:
- Título: Conference Papers
- Conference titles: Latin America Optics and Photonics Conference - LAOP
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
PATIÑO, Claudia Patricia Barrera et al. Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning. 2024, Anais.. Washington, DC: Optica Publishing Group - OPG, 2024. Disponível em: https://doi.org/10.1364/LAOP.2024.Tu4A.20. Acesso em: 11 fev. 2026. -
APA
Patiño, C. P. B., Soares, J. M., Blanco, K. C., Inada, N. M., & Bagnato, V. S. (2024). Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning. In Conference Papers. Washington, DC: Optica Publishing Group - OPG. doi:10.1364/LAOP.2024.Tu4A.20 -
NLM
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning [Internet]. Conference Papers. 2024 ;[citado 2026 fev. 11 ] Available from: https://doi.org/10.1364/LAOP.2024.Tu4A.20 -
Vancouver
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Identification of antibiotic resistance susceptibility in different species of microorganisms implementing machine learning [Internet]. Conference Papers. 2024 ;[citado 2026 fev. 11 ] Available from: https://doi.org/10.1364/LAOP.2024.Tu4A.20 - Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms
- Spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm
- Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification
- Time evolution of bacterial resistance observed with principal component analysis
- Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms
- FTIR-derived feature insights for predicting time-dependent antibiotic resistance progression
- Study of the action of curcuminoids in the photodynamic inactivation of bacteria resistant to antibiotics
- Machine learning applied to analyses of FTIR spectrum to identification of antibiotic resistance in different species of microorganisms
- Advances in the clinical application of photodynamic action for pharyngotonsillitis treatment (Conference Presentation)
- Prevention of rheumatic fever by continuous photodynamic therapeutic
Informações sobre o DOI: 10.1364/LAOP.2024.Tu4A.20 (Fonte: oaDOI API)
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