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Prediction of bacterial resistance/susceptibility to antimicrobials (from genotypic data) using Machine Learning methods is a problem that has started receiving interest in recent years.

The following paper (from 2017) analysed the then current literature and found that:

To date, there has not been a consensus about the optimal machine learning model to be used for AST genotype–phenotype prediction, as reflected by the diverse algorithms authors have implemented (Table 1).

Has this changed in the past 2 years?

Is there now a consensus about which models are most effective?


Table 1 from paper: enter image description here

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  • $\begingroup$ Models will keep improving as more experimental data gets generated. So the best will change over time. I haven't looked at all these models but depending on their complexity, they may or may not need major revision. Any answer to this question will get outdated soon. $\endgroup$ – WYSIWYG Mar 15 at 21:21

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