Explainable rule-based prediction of cultivation media for microbes.

Knowledge of microbial growth preferences remains dispersed-often confined to research articles or human experts-making new experiment design heavily reliant on manual expertise and literature searches. While previous computational efforts have explored media prediction through phylogenetic similarity or leveraged genomic data for trait modeling, they often produce predictions whose underlying biological rationale is not transparent or rely on biased features (e.g., incomplete genome annotations). To address this need for greater interpretability, we used the recently introduced KG-Microbe knowledge graph, a harmonized resource of microbial organismal traits and other properties, to explain growth media preferences. We employed explainable methods by developing a simple, rule-based classifier from these traits and compared its performance and interpretative power to that of a high-performing black-box model. While the black-box model showed slightly higher overall predictive performance, the transparency of the rule-based system and its ability to generate verifiable, biologically plausible rules make it a more sustainable and insightful framework. To explore feature importance, we applied SHAP to the black-box model and compared the results with a rule-based feature-importance method. Finally, leveraging the resulting rule set-together with insights from a large language model (LLM) and domain expertise-we propose strategies to advance microbial research. Code, models, and results are available at https://github.com/culturebotai/microbe-rules.