# Supervised machine learning for microbiomics: bridging the gap between current and best practices

arXiv: https://arxiv.org/abs/2402.17621 · HTML: https://mlsanity.com/paper/2402.17621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 144 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Reconstructing Training Data with Informed Adversaries](https://mlsanity.com/paper/2201.04845/) (2022)
- [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022)
- [Data-driven Advice for Applying Machine Learning to Bioinformatics Problems](https://mlsanity.com/paper/1708.05070/) (2017)
- [Reconstructing Training Data from Diverse ML Models by Ensemble Inversion](https://mlsanity.com/paper/2111.03702/) (2021)
- [Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome](https://mlsanity.com/paper/2205.09906/) (2022)

## Abstract (arXiv)

Machine learning (ML) is poised to drive innovations in clinical microbiomics, such as in disease diagnostics and prognostics. However, the successful implementation of ML in these domains necessitates the development of reproducible, interpretable models that meet the rigorous performance standards set by regulatory agencies. This study aims to identify key areas in need of improvement in current ML practices within microbiomics, with a focus on bridging the gap between existing methodologies and the requirements for clinical application. To do so, we analyze 100 peer-reviewed articles from 2021-2022. Within this corpus, datasets have a median size of 161.5 samples, with over one-third containing fewer than 100 samples, signaling a high potential for overfitting. Limited demographic data further raises concerns about generalizability and fairness, with 24% of studies omitting participants' country of residence, and attributes like race/ethnicity, education, and income rarely reported (11%, 2%, and 0%, respectively). Methodological issues are also common; for instance, for 86% of studies we could not confidently rule out test set omission and data leakage, suggesting a strong potential for inflated performance estimates across the literature. Reproducibility is also a concern, with 78% of studies abstaining from sharing their ML code publicly. Based on this analysis, we provide guidance to avoid common pitfalls that can hinder model performance, generalizability, and trustworthiness. An interactive tutorial on applying ML to microbiomics data accompanies the discussion, to help establish and reinforce best practices within the community.