A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles.
This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design.
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA.
David E
DE
Jones
Departments of Bioengineering and Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112, USA; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA.
Hamidreza
H
Ghandehari
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: julio.facelli@utah.edu.
Julio C
JC
Facelli
D000818:Animals; D002460:Cell Line; D057225:Data Mining; D006801:Humans; D000069550:Machine Learning; D053758:Nanoparticles
Data mining; Machine learning; Nanoinformatics; Nanomedicine