Work

Merging data curation and machine learning to improve nanomedicines

Year: 2022

Type: review

Source: Advanced Drug Delivery Reviews

DOI: 10.1016/j.addr.2022.114172

Authors: Chen Chen, Zvi Yaari, Elana Apfelbaum, Piotr Grodzinski, Yosi Shamay, Daniel A. Heller

Institutions: Memorial Sloan Kettering Cancer Center, Tri-Institutional PhD Program in Chemical Biology, Cornell University, Weill Cornell Medicine, National Institutes of Health, National Cancer Institute, Technion – Israel Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke

Awards: Tumor-Selective Delivery Approaches for Medulloblastoma, G2377374449, G372934033, Nanotechnology as a therapeutic approach in arteriovenous fistula maturation, P-selectin-Mediated Targeting of PI3K Nanomedicines to the Tumor Microenvironment, Renal tubule-specific nanotherapeutics for acute kidney injury, Cancer Center Support Grant

OpenAlex ID: W4212821988

FWCI: 1.498

Cites: 125

Cited by: 118

Related to: 10