Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)-Reference-Cited by-同舟云学术

Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)

Published:2021 Issue:35 Volume:13 Page:14666-14678
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Gomes Susana I. L.¹^ORCID,Amorim Mónica J. B.¹^ORCID,Pokhrel Suman²³^ORCID,Mädler Lutz²³^ORCID,Fasano Matteo⁴^ORCID,Chiavazzo Eliodoro⁴^ORCID,Asinari Pietro⁴⁵^ORCID,Jänes Jaak⁶,Tämm Kaido⁶^ORCID,Burk Jaanus⁶,Scott-Fordsmand Janeck J.⁷

Affiliation:

1. Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal

2. Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany

3. Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany

4. Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy

5. INRIM, Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, Torino 10135, Italy

6. Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia

7. Department of Bioscience, Aarhus University, Vejlsovej 25, PO BOX 314, DK-8600 Silkeborg, Denmark

Abstract

Based on a highly detailed materials characterisation database (including atomistic and multiscale modelling), single and univariate statistical methods, combined with machine learning techniques, revealed key descriptors of biological functions.

Funder

European Regional Development Fund

Horizon 2020 Framework Programme

Fundação para a Ciência e a Tecnologia

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2021/NR/D1NR03231C

Reference54 articles.

1. In vitro evaluation of cytotoxicity of engineered metal oxide nanoparticles