Plate visualization allows initial visual inspection of HTS and HC datasets to evaluate the consistency of sample replicates and the effectiveness of positive/negative controls. Rapid data visualization can then guide the selection of suitable statistical methods for subsequent data analysis.
Hit-identification can be performed to identify samples of high bioactivity (i.e. ‘hits’) for further confirmatory tests, as well as toxicity response interpretation and modeling. Heat maps can subsequently be generated to depict sample bioactivity (summarized HTS data) in a color map for convenient visual inspection.
Outlier removal from experimental HTS datasets may be required in order to exclude abnormal values (i.e. statistically inconsistent and thus unlikely to belong to the dataset), thus ensuring robust and reliable inference of ENM toxicity. HTS plate normalization is essential in order to account for plate-to-plate variability, remove systematic errors (e.g. positional effects) and compare/combine data from different plates.
Clustering can be performed to extract information that is useful for ENM risk assessment and decision making. For example, SOM clustering analysis can group together ENMs of similar HTS bioactivity profiles, indicating that these ENMs might share common toxicity mechanisms.
ToxNano provides a suit of general purpose data driven models for (1) evaluating the body of evidence, (2) assessing the significance of ENMs attributes correlating with toxicity, (3) integrating information (qualitative and quantitative), (4) predictive toxicology, and (5) tiered approaches for correlating ENMs properties and experimental settings with toxicity.
A variety of structure-activity relationships are currently available in ToxNano for a wide range of ENMs including, metal, metal oxides and Quantum Dots (QD), as well as various surface modified ENMs. ToxNano is designed to allow for the addition of new toxicity models as the body of knowledge expands
A particular challenge in toxicity evaluation is to assess if advanced literature data mining/knowledge-extraction techniques can be implemented for use where a sufficiently large body of literature exists, instead of using exhaustive parametric experimental studies in an era of limited resources. ToxNano provides the approach to mining the large body of evidence in order to answer the following: 1) Are the available literature data for a specific ENMs sufficient, to unambiguously identify relevant parameters that govern ENM toxicity? and 2) Can literature data mining and knowledge extraction be used to identify gaps and suggest possible approaches to streamline toxicity studies?
Hierarchical Clustering can be performed to identify groups of ENMs of similar HTS bioactivity profiles. This helps to visually integrate the information and quantitatively identify common toxicity mechanisms.
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