Detection of resistivity for antibiotics by probabilistic neural networks.
This paper presents the use of probabilistic neural networks (PNNs) for detection of resistivity for antibiotics (resistant and sensitive). The PNN is trained on the resistivity or sensitivity to the antibiotics of each record in the Salmonella database. Estimation of the whole parameter space for the PNN was performed by the maximum-likelihood (ML) estimation method. The expectation-maximization (EM) approach can help to achieve the ML estimation via iterative computation. Resistivity and sensitivity of the three antibiotics (ampicillin, chloramphenicol disks and trimethoprim-sulfamethoxazole) were classified with high accuracies by the PNN. The obtained results demonstrated the success of the PNN to help in detection of resistivity for antibiotics.
Budak
Fatma
F
Departmant of Microbiology, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380 Kocaeli, Turkey.
Ubeyli
Elif Derya
ED
D000900:Anti-Bacterial Agents / Q000494:pharmacology; D016208:Databases, Factual; D024881:Drug Resistance, Bacterial; D006801:Humans; D016013:Likelihood Functions; D008826:Microbial Sensitivity Tests / Q000295:instrumentation* / Q000379:methods*; D016571:Neural Networks, Computer; D011336:Probability; D012372:ROC Curve; D012475:Salmonella / Q000187:drug effects
D000900:Anti-Bacterial Agents