Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms (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
- Subjects: APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE PADRÕES; RESISTÊNCIA MICROBIANA ÀS DROGAS; ESPECTROSCOPIA INFRAVERMELHA
- Language: Inglês
- Imprenta:
- Publisher: Institute of Physics - IOP
- Publisher place: Bristol
- Date published: 2024
- Source:
- Título: Program
- Conference titles: International Laser Physics Workshop - LPHYS'24
-
ABNT
PATIÑO, Claudia Patricia Barrera et al. Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms. 2024, Anais.. Bristol: Institute of Physics - IOP, 2024. Disponível em: https://www.lasphys.com/workshops/abstracts/files/2024/7c/8e/af/3750dbeedd54047b99a5070e2e/abstract.pdf. Acesso em: 29 dez. 2025. -
APA
Patiño, C. P. B., Soares, J. M., Blanco, K. C., Inada, N. M., & Bagnato, V. S. (2024). Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms. In Program. Bristol: Institute of Physics - IOP. Recuperado de https://www.lasphys.com/workshops/abstracts/files/2024/7c/8e/af/3750dbeedd54047b99a5070e2e/abstract.pdf -
NLM
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms [Internet]. Program. 2024 ;[citado 2025 dez. 29 ] Available from: https://www.lasphys.com/workshops/abstracts/files/2024/7c/8e/af/3750dbeedd54047b99a5070e2e/abstract.pdf -
Vancouver
Patiño CPB, Soares JM, Blanco KC, Inada NM, Bagnato VS. Identification of antibiotic resistance in FTIR spectra of bacteria with machine learning algorithms [Internet]. Program. 2024 ;[citado 2025 dez. 29 ] Available from: https://www.lasphys.com/workshops/abstracts/files/2024/7c/8e/af/3750dbeedd54047b99a5070e2e/abstract.pdf - Implementation of machine learning study in Staphylococcus aureus’s FTIR spectra to antibiotic resistance identification
- 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
- FTIR-derived feature insights for predicting time-dependent antibiotic resistance progression
- Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms
- Time evolution of bacterial resistance observed with principal component analysis
- Study of the action of curcuminoids in the photodynamic inactivation of bacteria resistant to antibiotics
- Advances in the clinical application of photodynamic action for pharyngotonsillitis treatment (Conference Presentation)
- Evolution of surviving Streptoccocus pyogenes from pharyngotonsillitis patients submit to multiple cycles of antimicrobial photodynamic therapy
- Prevention of rheumatic fever by continuous photodynamic therapeutic
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