Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles

The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a...

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Detalles Bibliográficos
Autores principales: Yu, Fubo, Wei, Changhong, Deng, Peng, Peng, Ting, Hu, Xiangang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2021
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8153727/
https://www.ncbi.nlm.nih.gov/pubmed/34039604
http://dx.doi.org/10.1126/sciadv.abf4130

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8153727/
https://www.ncbi.nlm.nih.gov/pubmed/34039604
http://dx.doi.org/10.1126/sciadv.abf4130

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