ArticleBeilstein journal of nanotechnology2015
The eNanoMapper database for nanomaterial safety information.
Article in Beilstein journal of nanotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicityNanoscale advances · 2026Article
- Automated Large-Scale Extraction of Nanotoxicity Data from the Literature Using Multimodal Large Language Models.Computational and structural biotechnology journal · 2026Article
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Nanoscale reference and test materials for the validation of characterization methods for engineered nanomaterials - current state, limitations, and needs.Analytical and bioanalytical chemistry · 2025Review
- Discovering New Tyrosinase Inhibitors by Using In Silico Modelling, Molecular Docking, and Molecular Dynamics.Pharmaceuticals (Basel, Switzerland) · 2025Article
- 25 years of research and regulation: Is nanotechnology safe to commercialize?Frontiers in toxicology · 2025Review
- Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics.Small methods · 2024Article
- Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance.Computational and structural biotechnology journal · 2024Article
- An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.Nano letters · 2024Article
- FAIR assessment of nanosafety data reusability with community standards.Scientific data · 2024Article
- From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials.Journal of cheminformatics · 2024Article
- Mining, refining, and QSAR analysing the nanoinformatics in EPA NaKnowBase.Environmental science. Nano · 2024Article
- Knowledge and Instance Mapping: architecture for premeditated interoperability of disparate data for materials.Scientific data · 2024Article
- Translating nanoEHS data using EPA NaKnowBase and the resource description framework.F1000Research · 2024Article
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe NanoSafety Cluster, a cluster of projects funded by the European Commision, identified the need for a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs). Ontologies, open standards, and interoperable designs were envisioned to empower a harmonized approach to European research in nanotechnology. This setting provides a number of opportunities and challenges in the representation of nanomaterials data and the integration of ENM information originating from diverse systems. Within this cluster, eNanoMapper works towards supporting the collaborative safety assessment for ENMs by creating a modular and extensible infrastructure for data sharing, data analysis, and building computational toxicology models for ENMs.
resultsThe eNanoMapper database solution builds on the previous experience of the consortium partners in supporting diverse data through flexible data storage, open source components and web services. We have recently described the design of the eNanoMapper prototype database along with a summary of challenges in the representation of ENM data and an extensive review of existing nano-related data models, databases, and nanomaterials-related entries in chemical and toxicogenomic databases. This paper continues with a focus on the database functionality exposed through its application programming interface (API), and its use in visualisation and modelling. Considering the preferred community practice of using spreadsheet templates, we developed a configurable spreadsheet parser facilitating user friendly data preparation and data upload. We further present a web application able to retrieve the experimental data via the API and analyze it with multiple data preprocessing and machine learning algorithms.
conclusionWe demonstrate how the eNanoMapper database is used to import and publish online ENM and assay data from several data sources, how the "representational state transfer" (REST) API enables building user friendly interfaces and graphical summaries of the data, and how these resources facilitate the modelling of reproducible quantitative structure-activity relationships for nanomaterials (NanoQSAR).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.