Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
IPCAT '97 Proceedings of the second international workshop on Information processing in cell and tissues
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Median power and median correlation theory
IEEE Transactions on Signal Processing
A general weighted median filter structure admitting negativeweights
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
Microarray technology is revolutionizing functional genomics research by allowing scientists to measure the expression level of thousands of genes simultaneously from a single sample. However, a standard protocol for microarray data analysis has yet to be established. Many analysis techniques currently rely on linear correlation between pairs of genes. Such analysis only detects relationships in signal components exhibiting Gaussian correlation statistics. This paper focuses on determining the relationships between gene patterns based on a hierarchical clustering of nonlinear correlation measurements. The methods described herein are illustrated with gene expression data from yeast, and from human cancer cell lines. The results indicate that in some cases, improved clustering of genes can be achieved by the use of nonlinear correlation metrics.