Toxicity risk assessment from heterogeneous uncertain data with possibility-probability distribution

Due to the advance of modern computing technology, decisions can be made based on all the existing related data instances scattered across multiple data storages, such that available information has been entirely taken into consideration. Particularly in the predictive toxicology domain, because of the heterogeneity of data sources, multiple data instances with respect to the same endpoint are usually inconsistent, and the quality (or reliability) of the data instances is typically different. Also, the quantity of data instances is often not sufficient to conduct a study using conventional statistics-based methods. This paper presents a novel risk analysis approach for chemical toxicity assessment which considers all the available heterogeneous data instances in the same time, assisted by their quality (or reliability) values. The system is developed on the basis of possibility-probability distribution, where the uncertainty of the approximated probability values based on traditional statistics methods is represented by possibility. The uncertainty considered herein is led not only by the statistics on limited small number of data instances, but also by the poor quality (or reliability) of data instances. The possibility-probability distribution is automatically computed from available data instances by employing a modified diffused-interior-outer-set model (where the reliability of data is considered) based on information diffusion theory. Toxicity value for a given chemical compound is then estimated as the fuzzy expected value based on the resulted possibility-probability distribution. Toxicity risk with respect to regulatory threshold is also introduced, in order to evaluate the probability of which the toxicity may be classified into a certain regulatory range. The proposed approach is applied to a real-world dataset to illustrate the utility and the potential of the approach in risk assessment of chemical toxicity.