TY - JOUR
T1 - Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium
AU - Li, Huankai
AU - Chen, Leijian
AU - Zhang, Feng
AU - Cai, Zongwei
N1 - Funding Information:
The corresponding author would like to thank the Kwok Chung Bo Fun Charitable Fund for the establishment of the Kwok Yat Wai Endowed Chair of Environmental and Biological Analysis.
Publisher Copyright:
© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2025/1
Y1 - 2025/1
N2 - 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 R2 > 0.96 and test R2 > 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.
AB - 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 R2 > 0.96 and test R2 > 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.
KW - Artificial intelligence
KW - Cultivation optimization
KW - Illumination intensity
KW - Porphyridium cultivation
UR - http://www.scopus.com/inward/record.url?scp=85208475429&partnerID=8YFLogxK
U2 - 10.1016/j.biortech.2024.131728
DO - 10.1016/j.biortech.2024.131728
M3 - Journal article
C2 - 39521188
AN - SCOPUS:85208475429
SN - 0960-8524
VL - 416
JO - Bioresource Technology
JF - Bioresource Technology
M1 - 131728
ER -