Article
Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties.
ACS applied bio materials - 16 Oct 2023
Ma Xingqun, Tang Yuxia, Wang Chuanbing, Li Yang, Zhang Jiulou, Luo Yafei, Xu Ziqing, Wu Feiyun, Wang Shouju
Abstract excerpt
Understanding the complex interaction between nanoparticles (NPs) and tumors in vivo and how it dominates the delivery efficiency of NPs is critical for the translation of nanomedicine. Herein, we proposed an interpretable XGBoost-SHAP model by integrating the information on NPs physicochemical properties and tumor genomic profile to predict the delivery efficiency. The correlation coefficients were 0.66, 0.75,...
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