ART 2-A for optimal test series design in QSAR

Author(s): Buydens, L. | Devillers, J. | Domine, D.M.C. | Wienke, D. |

Year: 1997

Citation: Journal of Chemical Information and Computer Sciences 37(1): 10-17

Abstract: The family of adaptive resonance theory (ART) based systems concerns distinct artificial neural networks for unsupervised and supervised clustering analysis. Among them, the ART 2-A paradigm presents numerous strengths for data analysis. After a rapid presentation of the ART 2-A theory and algorithmic information, the usefulness of this neural network for the selection of optimal test series is estimated. The results are compared with those obtained from hierarchical cluster analysis and visual mapping methods. The advantages and drawbacks of each method are discussed. We show that ART 2-A represents a new useful nonlinear statistical tool for QSAR and drug design.

Topics: Machine Learning, Applications: Industrial Control, Market Research, Medical Diagnosis, Models: ART 2-A,

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