不可用
计算机科学
领域(数学分析)
外推法
数据集成
传感器融合
大数据
透视图(图形)
数据挖掘
数据整理
数据科学
风险分析(工程)
机器学习
人工智能
可靠性工程
工程类
医学
数学分析
数学
作者
Longzhi Yang,Daniel Neagu
标识
DOI:10.1109/ukci.2014.6930153
摘要
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.
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