Identification and prediction of bacterial antibiotic resistance via genomic data analysis

Abstract:

Using genomic data and bioinformatics methods has become an important approach to rapidly identify the genes and predict the phenotypes of bacterial antibiotic resistance. Dozens of antibiotic resistance databases have been established, providing information and auxiliary tools for the identification and prediction of bacterial antibiotic resistance. As the bacterial genome data and antibiotic resistance phenotype data are increasing, the correlation between them can be established via big data and machine learning. Therefore, establishing efficient models predicting antibiotic resistance phenotypes has become a research hot topic. Focusing on the gene identification and phenotype prediction of bacterial antibiotic resistance, this review discusses the related databases, the theories and methods, the machine learning algorithms, and the prediction models. In addition, we made an outlook on the future prospects in this field, aiming to provide new ideas for the related studies.