Knowledge Discovery From Sparse Pharmacokinetic Data

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

doi:10.1145/335603.335699

Knowledge Discovery From Sparse Pharmacokinetic Data

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

Computer Science

Research output: Contribution to book or proceeding › Chapter

4 Scopus citations

Abstract

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.

Original language	American English
Title of host publication	Proceedings of The 2000 ACM International Symposium on Applied Computing (SAC'00)
DOIs	https://doi.org/10.1145/335603.335699
State	Published - Mar 2000

Keywords

Discovery
Knowledge
Sparse pharmacokinetic data

DC Disciplines

Computer Sciences
Engineering

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10.1145/335603.335699License: Unspecified

Cite this

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AB - 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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KW - Sparse pharmacokinetic data

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M3 - Chapter

BT - Proceedings of The 2000 ACM International Symposium on Applied Computing (SAC'00)

ER -

Knowledge Discovery From Sparse Pharmacokinetic Data

Abstract

Keywords

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