Selective detection of complex gas mixtures using point contacts: concept, method and tools

• Alexander P. Pospelov,
• Victor I. Belan,
• Dmytro O. Harbuz,
• Volodymyr L. Vakula,
• Lyudmila V. Kamarchuk,
• Yuliya V. Volkova and
• Gennadii V. Kamarchuk

Beilstein J. Nanotechnol. 2020, 11, 1631–1643, doi:10.3762/bjnano.11.146

• reagents produced by the NPL Granum company (Kharkiv, Ukraine). Statistical calculations were made using the WEKA data mining software [57]. Additional control and verification of the results were performed with the SPSS statistical package [58]. The data were processed statistically using the standard

Nanoinformatics for environmental health and biomedicine

• Rong Liu and
• Yoram Cohen

Beilstein J. Nanotechnol. 2015, 6, 2449–2451, doi:10.3762/bjnano.6.253

• , literature mining for nano-data collection and meta-analysis, data mining/machine learning of nano-data (e.g., development of quantitative structure–activity relationships (QSARs)), simulation of the fate and transport of nanomaterials, nano-bio interactions, and assessment of potential environmental and

• for brain cancer [13]. As an imported aspect of nanoinformatics, recent advances in data mining/machine learning of nano-data are also reported in this Thematic Series. In one study, the toxicity of ZnO nanoparticles to zebrafish (measured by mortality rate (%)) was correlated to two principal

Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors

• David E. Jones,
• Hamidreza Ghandehari and
• Julio C. Facelli

Beilstein J. Nanotechnol. 2015, 6, 1886–1896, doi:10.3762/bjnano.6.192

• , Salt Lake City, UT 84112, USA, Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA 10.3762/bjnano.6.192 Abstract The use of data mining techniques in the field of nanomedicine has been very limited. In this paper we demonstrate that data mining

• journal articles. The results indicate that data mining and machine learning can be effectively used to predict the cytotoxicity of PAMAM dendrimers on Caco-2 cells. Keywords: data mining; machine learning; molecular descriptors; poly(amido amine) dendrimers (PAMAM); Introduction In silico approaches

• , such as data mining and machine learning, have been very successful in medicinal chemistry and are commonly used to guide the design of small pharmaceutical compounds [1]. In contrast, although nanomedicine is a rapidly growing field [2], there have been only a few attempts to use data mining

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

• , data mining and machine learning algorithms and methods for defining the applicability domain of a predictive model. A screenshot of the Jaqpot web services is presented in Figure 19. Jaqpot provides asynchronous execution of tasks submitted by users, authentication, authorisation and accounting

Using natural language processing techniques to inform research on nanotechnology

• Nastassja A. Lewinski and
• Bridget T. McInnes

Beilstein J. Nanotechnol. 2015, 6, 1439–1449, doi:10.3762/bjnano.6.149

• online repositories to broaden participation in nanoinformatics. Keywords: data mining; informatics; name entity recognition; nano-informatics; nanoparticles; nanotechnology; nanotoxicity; natural language processing; text mining; Introduction Nanotechnology may still be considered a relatively new