Created by W.Langdon from gp-bibliography.bib Revision:1.7185
The complications associated with infections from pathogens increasingly resistant to traditional drugs lead to a constant increase in the mortality rate among those affected. In such cases the fundamental purpose of the microbiology laboratory is to determine the sensitivity profile of pathogens to antimicrobial agents. This is an intense and complex work often not facilitated by the test's characteristics. Despite the evolution of the Antimicrobial Susceptibility Testing (AST) technologies, the technological breakthrough that could guide and facilitate the search for new antimicrobial agents is still missing.
In this work, we propose the experimental use of in silico instruments, particularly feedforward Multi-Layer Perceptron (MLP) Artificial Neural Network, and Genetic Programming (GP), to verify, but also to predict, the effectiveness of natural and experimental mixtures of polyphenols against several microbial strains.
We value the results in predicting the antimicrobial sensitivity profile from the mixture data. Trained MLP shows very high correlations coefficients (0,93 and 0,97) and mean absolute errors (110,70 and 56,60) in determining the Minimum Inhibitory Concentration and Minimum Microbicidal Concentration, respectively, while GP not only evidences very high correlation coefficients (0,89 and 0,96) and low mean absolute errors (6,99 and 5,60) in the same tasks, but also gives an explicit representation of the acquired knowledge about the polyphenol mixtures.
In silico tools can help to predict phytobiotics antimicrobial efficacy, providing an useful strategy to innovate and speed up the extant classic microbiological techniques.",
Genetic Programming entries for Salvatore Rampone Caterina Pagliarulo Chiara Marena Antonello Orsillo Margherita Iannaccone Carmela Trionfo Daniela Sateriale Marina Paolucci