Supervised machine learning for microbiomics: bridging the gap between
  current and best practices
Natasha K. Dudek Mariam Chakhvadze Saba Kobakhidze Omar Kantidze Yuriy Gankin
Machine learning (ML) is set to accelerate innovations in clinical
microbiomics, such as in disease diagnostics and prognostics. This will require
high-quality, reproducible, interpretable workflows whose predictive
capabilities meet or exceed the high thresholds set for clinical tools by
regulatory agencies. Here, we capture a snapshot of current practices in the
application of supervised ML to microbiomics data, through an in-depth analysis
of 100 peer-reviewed journal articles published in 2021-2022. We apply a
data-driven approach to steer discussion of the merits of varied approaches to
experimental design, including key considerations such as how to mitigate the
effects of small dataset size while avoiding data leakage. We further provide
guidance on how to avoid common experimental design pitfalls that can hurt
model performance, trustworthiness, and reproducibility. Discussion is
accompanied by an interactive online tutorial that demonstrates foundational
principles of ML experimental design, tailored to the microbiomics community.
Formalizing community best practices for supervised ML in microbiomics is an
important step towards improving the success and efficiency of clinical
research, to the benefit of patients and other stakeholders.