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Nowadays, the rapid identification of bacterial antibiotic resistance is one of the major biomedical challenges. Classical methods of detection (culture and sensitivity testing, microbial whole-genome sequencing) fail in the context of time requirements. In this work, we propose the express method for the detection of gene encoding enzyme responsible for bacterial antibiotic resistance. Proposed analytical approach is based on a combination of unique advantages provided by surface enhanced Raman spectroscopy (SERS) and artificially created convolutional neural network (CNN). SERS is known for the extremely high sensitivity and fast analysis, while CNN seems to be a promising alternative to find even ambiguous spectral properties produced by the Raman signal.
Keywords: SERS, DNA, neural networks, CNN, antibiotic resistance© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.