论文信息 - Knowledge discovery from sparse pharmacokinetic data

Knowledge discovery from sparse pharmacokinetic data

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.

Ray R. Hashemi | Charles Epperson | Alexander A. Tyler | John F. Young

[1] John R. Anderson,et al. MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[2] Ray R. Hashemi,et al. A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems , 1997 .

[3] F R Jelovsek,et al. Developmental toxicity risk assessment: a rough sets approach. , 1993, Methods of information in medicine.

[4] Jerzy W. Grzymala-Busse,et al. LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[5] Zdzislaw Pawlak,et al. Rough classification , 1984, Int. J. Hum. Comput. Stud..

[6] Ryszard S. Michalski,et al. A theory and methodology of inductive learning , 1993 .