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Proceedings Paper

A new data mining tool for analyzing coumarin-based prodrugs
Author(s): Hao Fang; Jun Li; Yi Sun; Binghe Wang; Yan-Qing Zhang
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Paper Abstract

This paper focuses on using fuzzy neural network data mining techniques to analyze nonlinear relations among chemical factors. Through standardizing and rescaling the raw data, we processed the data into fuzzy neural network not only to learn chemical knowledge from large amounts of experimental data, but also predict future chemical parameters for further experimental verification. The results show that the most relative chemical factor can be obtained by analyzing the experimental errors using fuzzy rules.

Paper Details

Date Published: 12 April 2004
PDF: 11 pages
Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); doi: 10.1117/12.541595
Show Author Affiliations
Hao Fang, Georgia State Univ. (United States)
Jun Li, Georgia State Univ. (United States)
Yi Sun, Georgia State Univ. (United States)
Binghe Wang, Georgia State Univ. (United States)
Yan-Qing Zhang, Georgia State Univ. (United States)


Published in SPIE Proceedings Vol. 5433:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI
Belur V. Dasarathy, Editor(s)

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