Integration strategies for toxicity data from an empirical perspective

Yang, Longzhi and Neagu, Daniel (2014) Integration strategies for toxicity data from an empirical perspective. In: Computational Intelligence (UKCI), 2014 14th UK Workshop on. IEEE, Piscataway, NJ. ISBN 978-1-4799-5538-1

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Official URL: http://dx.doi.org/10.1109/UKCI.2014.6930153

Abstract

The recent development of information techniques, especially the state-of-the-art “big data” solutions, enables the extracting, gathering, and processing large amount of toxicity information from multiple sources. Facilitated by this technology advance, a framework named integrated testing strategies (ITS) has been proposed in the predictive toxicology domain, in an effort to intelligently jointly use multiple heterogeneous toxicity data records (through data fusion, grouping, interpolation/extrapolation etc.) for toxicity assessment. This will ultimately contribute to accelerating the development cycle of chemical products, reducing animal use, and decreasing development costs. Most of the current study in ITS is based on a group of consensus processes, termed weight of evidence (WoE), which quantitatively integrate all the relevant data instances towards the same endpoint into an integrated decision supported by data quality. Several WoE implementations for the particular case of toxicity data fusion have been presented in the literature, which are collectively studied in this paper. Noting that these uncertainty handling methodologies are usually not simply developed from conventional probability theory due to the unavailability of big datasets, this paper first investigates the mathematical foundations of these approaches. Then, the investigated data integration models are applied to a representative case in the predictive toxicology domain, with the experimental results compared and analysed.

Item Type: Book Section
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Nicola King
Date Deposited: 11 Dec 2014 14:39
Last Modified: 10 Nov 2016 12:38
URI: http://nrl.northumbria.ac.uk/id/eprint/18470

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