Nina Jeliazkova

TL;DR: A review of ontology developments in the field of predictive toxicology can be found in this article, where the authors present a set of perspectives showing how ontologies are being used in toxicology initiatives and applications.

TL;DR: The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-SAR, and present opinions on the criteria to be fulfilled for models developed for nanoparticles.

TL;DR: A combination of approaches towards lowering the computational cost and improving the response time of substructure queries are described, which aim to achieve better performance and compatibility with multiple existing flavours of the SMARTS language.

TL;DR: The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications.

TL;DR: The OpenTox Framework as mentioned in this paper provides a unified access to toxicity data, predictive models and validation procedures using a common information model, based on the openTox ontologies, describing predictive algorithms, models and toxicity data.