Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties-Reference-Cited by-同舟云学术

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

Published:2023-09-08 Issue:10 Volume:6 Page:4326-4335
ISSN:2576-6422
Container-title:ACS Applied Bio Materials
language:en
Short-container-title:ACS Appl. Bio Mater.

Author:

Ma Xingqun¹²,Tang Yuxia¹,Wang Chuanbing¹,Li Yang¹,Zhang Jiulou¹,Luo Yafei¹,Xu Ziqing¹,Wu Feiyun¹,Wang Shouju¹^ORCID

Affiliation:

1. Laboratory of Molecular Imaging, Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China

2. Department of Oncology, Nanjing Baiyi Hospital, Jinling Clinical College of Nanjing Medical University, Nanjing, Jiangsu 210000, China

Funder

China Pharmaceutical University

Natural Science Foundation of Jiangsu Province

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Biochemistry (medical),Biomedical Engineering,General Chemistry,Biomaterials

Link

https://pubs.acs.org/doi/pdf/10.1021/acsabm.3c00527

Reference30 articles.

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4. Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach

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