Image Analysis and Deep Learning Web Services for Nano informatics

Papadiamantis, Anastasios G.; Afantitis, Antreas; Tsoumanis, Andreas; Karatzas, Pantelis; Doganis, Philip; Varsou, Dimitra‐Danai; Sarimveis, Haralambos; Ellis, Laura‐Jayne A.; Valsami‐Jones, Eugenia; Lynch, Iseult; Melagraki, Georgia

doi:10.1002/9781119681397.ch28

Cited by 1 publication

(1 citation statement)

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“…At the same time, NanoCommons builds the European nanotechnology community informatics platform. In order to reach even larger scales and biological events, the approach being developed is to combine physicsbased multiscale modeling for the calculation of advanced descriptors and properties of NMs with nanoinformatics methods for the evaluation of their complex properties and functionalities [23,24].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Nanotecnology: How do they contribute to each other

Delfino¹,

Tebaldi²,

Dantas³

et al. 2023

Anais Da 2ª Conferência Brasileira De Planejamento Experimental E Análise De Dados: ConBraPA 2022

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It is undoubtedly of great impression how some of the areas of evolving Computational Sciences, such as Machine Learning and the great and vast nanotechnology domain, can contribute to all-world research. Some examples are nanotoxicology in medicine and food regulation, using predictive models to study the biological activity of nanomaterials, characterization of nanoparticles, and against COVID-19, with the development of vaccines and nanosensors. The relationship between nanotechnology and machine learning was investigated through bibliometric analysis using articles registered in the Scopus database. The keywords "Machine Learning" and "Nanotechnology" were searched in the Scopus database to develop this work. Two hundred sixty-seven scientific articles were gathered using the number of publications per year, area of knowledge, and countries as search criterions. Then, the titles and abstracts of these papers were stored in Research Information Systems (RIS) format and went through automatic analysis by the software "Visualization Of Similarities Method" (VOS viewer) version 1.6.18., which gave us a notion of link strength produced by their intercorrelation network with maps of Clusters, which was followed by a numerical criterion. Due to the limitation of VOSViewer to integrate and process the data in tables, tree maps, or graphics, Python can import and filter the data. So, it is possible to create new forms of visualizing data and the relationship between them using Python software. The trendline that describes the volume of publications as a function of the year assumes a third-order polynomial behavior, with R²=0.9716. Furthermore, the present work inferred the ten countries with the highest volume of publications that intersect the two areas between researched years. The United States, China, and the United Kingdom lead the volume of publications, with a significant difference between the amount of US publications and the subsequent ones.

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Section: Introductionmentioning

confidence: 99%