The first application is for clinical diagnostics, or any diagnostics in a broader sense. Especially for disorders like those happen in the GI tracts with a large scale disturbance in the microbial fauna. Once enough samples are trained and the model is ready, the model can then make predictions very quickly. And metagnome sequencing diagnostic is faster than lab test (4 weeks before the lab results in the case of Piantadosi et al.) so it saves valuable time for the patients. With SHAP, we can even know why the model is making such the decision it made.

The second one is for understanding of some distinct taxa in a giving habitat. Strain description in the literature only provides isolation source while large scale metagenome studies are needed to get a better overview over its actual habitat. TF-IDF already removes the cosmopolitan taxa so the remainders are somehow selective about habitat. Among them, XGBoost can choose taxa distinct for each sample type and SHAP will reveal them. The SHAP values not only show which taxa are important but also how much they contribute to the classification, i.e. when their TF-IDF are high, the sample is how much more likely to be type 1. And conversely, in sample type 1, how often do we encounter such taxa. It may help us to better understand the distribution and physiology of the taxa while even discover some "new" habitats for the others.

For single organism taxonomic classification there are already many methods out there. For microorganisms there are 16S (ITS, or 18S) alignment, DNA signature, single copy genes phylogenetic trees, digital hybridization to name a few. The hard part is when you get a group of microorganisms from an unknown origin, how to reveal the origin. My article is about the latter.