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Identification of adaptive mechanisms leading to reduced antibiotic susceptibility in bacterial biofilms using experimental evolution and machine learning approaches (01G00220)

Because many mechanisms of reduced sensitivity in bacterial biofilms are still unknown, it is impossible to predict resistance. In this project we will allow bacteria to evolve in vitro in the presence of antibiotics, in order to map all mutations, differences in gene expression and relevant phenotypic characteristics. This will allow to develop a prediction algorithm using machine learning.

Date:1 Jan 2020  →  Today
Keywords:antibiotic resistance, machine learning, microbial evolution, Biofilm
Disciplines:Machine learning and decision making, Infectious diseases, Bacteriology, Medicinal and biomolecular chemistry not elsewhere classified