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

arXiv:2402.17621v1 Announce Type: cross
Abstract: 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 …

abstract arxiv best practices capabilities clinical cs.lg current diagnostics disease gap innovations machine machine learning practices predictive q-bio.gn quality regulatory set supervised machine learning tools type will workflows