The application of DBF neural networks for object recognition
Information Sciences—Informatics and Computer Science: An International Journal
Study of Adaptive Equalizers Based on Two Weighted Neural Networks
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
A new development on ANN in China: biomimetic pattern recognition and multi weight vector neurons
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Continuous speech recognition based on ICA and geometrical learning
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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A common approach to the analysis of gene expression data is to define clusters of genes that have similar expression. A critical step in cluster analysis is the determination of similarity between the expression levels of two genes. We introduce a non-linear multi-weighted neuron-based similarity index and compare the results with other proximity measures for Saccharomyces cerevisiae gene expression data. We show that the clusters obtained using Euclidean distance, correlation coefficients, and mutual information were not significantly different. The clusters formed with the multi-weighted neuron-based index were more in agreement with those defined by functional categories and common regulatory motifs.