Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium

A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultivation parameters of Porphyridium. The BANE-XGBoost has the best prediction performance (train R-2 > 0.96 and test R-2 > 0.87). Based on Shapley Additive Explanation (SHAP) model, illumination intensity, culture time, and KH2PO4 are the most critical factors for Porphyridium growth. The combined facilitating roles of cultivation parameters are found using the SHAP value-based heat map and group. To reach high biomass and daily production rate concurrently, one-way and two-way partial dependent plots models find the optimal conditions. The top 2 critical parameters (illumination intensity and KH2PO4) were selected to verify using the graphical user interface website based on the optimized model and lab experiments, respectively. This study shows the graph-learning-based model can improve prediction performance and optimize intricate low-carbon microalgal cultivation.