A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles. Issue 132 (August 2016)

• Record Type: Journal Article
• Authors: Jones, David E.; Ghandehari, Hamidreza; Facelli, Julio C.
• Is Part Of: Computer methods and programs in biomedicine. Issue 132(2016)
• Page Start: 93
• Page End: 103
• Publication Date: 2016-08
• Subjects: Nanomedicine -- Nanoinformatics -- Data mining -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.04.025
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Review research using data mining and machine learning in nanomedicine. Examine progress and challenges in nanoinformatics. Abstract: 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.