Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties

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 prop...

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Veröffentlicht in:	ACS applied bio materials 2023-10, Vol.6 (10), p.4326-4335
Hauptverfasser:	Ma, Xingqun, Tang, Yuxia, Wang, Chuanbing, Li, Yang, Zhang, Jiulou, Luo, Yafei, Xu, Ziqing, Wu, Feiyun, Wang, Shouju
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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