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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 establishedvia 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.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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