PHLAME: A benchmark for continuous evaluation of host phenotype prediction from shotgun metagenomic data

Predicting host phenotypes from shotgun metagenomic data is essential for translating microbiome research into clinical practice. Despite the development of numerous computational tools for this task, researchers often default to traditional machine learning methods such as Random Forest. This hesitancy to adopt newer methods stems from their complexity as well as the lack of standardized evaluations, as most tools are assessed on different datasets and compared against a limited set of methods. To address this, we introduce PHLAME, a standardized benchmark for evaluating host phenotype prediction methods using gut metagenomic data. PHLAME features a diverse range of prediction tasks and enables consistent, comparative assessments across prediction tools. Our systematic evaluation of existing tools shows that microbiome-based phenotype prediction remains a challenging problem. However, we find that classic machine learning methods perform competitively, offering both ease of use and state-of-the-art results. PHLAME is publicly available for ongoing benchmarking at https://phlame.yassourlab.com/ .