Self-Organizing Maps
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Approach for Automated Gene Identification
Open Systems & Information Dynamics
Flexible architecture of self organizing maps for changing environments
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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DNA analysis by microarrays is a powerful tool that allows replication of the RNA of hundreds of thousands of genes at the same time, generating a large amount of data in multidimensional space that must be analyzed using informatics tools. Various clustering techniques have been applied to analyze the microarrays, but they do not offer a systematic form of analysis. This paper proposes the use of Gorban's Elastic Neural Net in an iterative way to find patterns of expressed genes. The new method proposed (Iterative Elastic Neural Net, IENN) has been evaluated with up-regulated genes of the Escherichia Coli bacterium and is compared with the Self-Organizing Maps (SOM) technique frequently used in this kind of analysis. The results show that the proposed method finds 86.7% of the up-regulated genes, compared to 65.2% of genes found by the SOM. A comparative analysis of Receiver Operating Characteristic (ROC) with SOM shows that the proposed method is 11.5% more effective.