Integration strategies for toxicity data from an empirical perspective

Longzhi Yang, Daniel Neagu

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
DOIs
Publication statusPublished - Nov 2014
Event14th UK Workshop on Computational Intelligence, UKCI 2014 - Bradford, UK
Duration: 1 Nov 2014 → …

Conference

Conference14th UK Workshop on Computational Intelligence, UKCI 2014
Period1/11/14 → …

Fingerprint Dive into the research topics of 'Integration strategies for toxicity data from an empirical perspective'. Together they form a unique fingerprint.

Cite this