A neural network-based similarity index for clustering DNA microarray data.

Tomohiro Sawa; Lucila Ohno-Machado

doi:10.1016/s0010-4825(02)00032-x

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A neural network-based similarity index for clustering DNA microarray data.

Tomohiro Sawa·Lucila Ohno-Machado

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Abstract

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 neural network-based similarity index as a non-linear 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 neural network-based index were more in agreement with those defined by functional categories and common regulatory motifs.

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Algorithms Cluster Analysis Computational Biology Gene Expression Regulation, Fungal Genes, Fungal Genetic Vectors Neural Networks, Computer Oligonucleotide Array Sequence Analysis Saccharomyces cerevisiae Journal Article Research Support, Non-U.S. Gov't

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Tomohiro Sawa, Lucila Ohno-Machado. 2003. A neural network-based similarity index for clustering DNA microarray data.. https://doi.org/10.1016/s0010-4825(02)00032-x

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