In this research effort a neural network approach was used as a method of extrapolating the presence of mercury in human blood from animal data. We also investigated the effect of different data representations (As-is, Category, Simple binary, Thermometer, and Flag) on the model performance. In addition, we used the Rough Sets methodology to identify the redundant independent variables and then examined the proposed extrapolation model performance for a reduced set of independent variables. Moreover, a quality measure was introduced that revealed that the proposed extrapolation model performed extremely well for the Thermometer data representation.

In this study, two algorithms (ONE and TWO) are introduced to determine the position of the t-distribution of variable V(i) (with 95% confidence) in the treated group in reference to the t-distribution of variable V(i) (with 95% confidence) in the control group of an experimental study involving UV radiation exposure of a group of rodents. The outcome of applying the two algorithms is two discretized files. A reduct of each file is generated using the rough sets methodology and then the measurements for one independent variable are predicted using the measurements of the other independent variables in the same reduct. The rough sets methodology and the fuzzy-rough classifier are used for this prediction. The results reveal that (1) algorithm TWO is the best, (2) the values for non-core variables are predicted with minimum accuracy of 87%, and (3) the prediction of values for core variables is not successful.

In this research effort, we show that the following hypothesis is true: The independently verified sparse information secured from the scientific literature regarding the effects of methyl mercury on mice enables us to predict the effects of the methyl mercury on humans.The Rough Sets methodology is used in this endeavor.

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