Details

Autor(en) / Beteiligte

• 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
• Roy, Kunal

Titel

Image Analysis and Deep Learning Web Services for Nano informatics

Ist Teil von

• Chemometrics and Cheminformatics in Aquatic Toxicology, 2021, p.547-564

Ort / Verlag

Hoboken, NJ, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Wiley Online Library All Obooks

Beschreibungen/Notizen

• In this chapter, we present and discuss the state of the art in image analysis tools for nanomaterials’ toxicology. These tools employ novel computational techniques and can contribute to the in silico characterization of materials in complex matrices and their toxicity assessment, by way of recognizing malformations in Daphnia magna . The tools presented were developed using well‐established computational platforms and complemented with an user‐friendly interface to allow even users without no informatics background to easily get acquainted with them and facilitating their routine use. The tools are freely available as cloud web‐applications, while it is also possible to use Application Programming Interfaces (APIs) to integrate the tools into external platforms or to download a containerized Docker application for local deployment. By the end of the chapter, readers will have a clear overview of the functionalities and abilities of the presented tools and the potential added value they offer in terms of working hours, experimental cost, and research outputs for in silico evaluation of nanomaterials toxicity as part of an integrated approach to testing and assessment.