Data Mining System for Biochemical Analysis in Experimental Physiology

Author(s): Aguilar, J. | AlDaraiseh, A. | Altamiranda, J. | Hernandez, L. |

Year: 2008

Citation: IEEE International Joint Conference on Neural Networks (IJCNN), VOLS 1-8 Pages: 3407-3411

Abstract: We develop a Data Mining system to assist with the elucidation by graphical means of the biochemical changes in the brains of rodents. Manual analysis of such experiments is increasingly impractical because of the voluminous nature of the data that is generated, and the tedious nature of the analysis means that important information can be missed. For this purpose we are constructing an increasingly sophisticated Data Mining system which contains a number of pre-processing stages and classification via two steps of an Adaptive Resonance Theory Artificial Neural Network. In this paper we describe the system. The focus of our activity Is the study of neurotransmitters: Glutamate and Aspartate and we present an example of how to utilize our Data Mining system for the automated classification of samples that are extracted from the brains of rodents. This methodology should prove equally valuable to other biochemical analysis problems in experimental Physiology.

Topics: Image Analysis, Applications: Biological Classification, Models: ART 2 / Fuzzy ART,

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