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    Big data in predictive toxicology

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    Publication date
    2019-12
    Author
    Neagu, Daniel
    Richarz, A-N.
    Keyword
    Toxicological data
    Big data
    Data science
    Toxicity prediction
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment.
    URI
    http://hdl.handle.net/10454/17603
    Version
    No full-text in the repository
    Citation
    Neagu D and Richarz A-N (Eds.) Big data in Predictive Toxicology. London: Royal Society of Chemistry.
    Link to publisher’s version
    https://doi.org/10.1039/9781782623656
    Type
    Book
    Collections
    Engineering and Informatics Publications

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