Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Clustering gene expression patterns
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Identifying Non-random Patterns from Gene Expression Profiles
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Spectral preprocessing for clustering time-series gene expressions
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
A data mining framework for time series estimation
Journal of Biomedical Informatics
OPTOC-based clustering analysis of gene expression profiles in spectral space
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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Many algorithms have been used to cluster genes measured by microarray across a time series. Instead of clustering, our goal was to compare all pairs of genes to determine whether there was evidence of a phase shift between them. We describe a technique where gene expression is treated as a discrete time-invariant signal, allowing the use of digital signal-processing tools, including power spectral density, coherence, and transfer gain and phase shift. We used these on a public RNA expression set of 2467 genes measured every 7 min for 119 min and found 18 putative associations. Two of these were known in the biomedical literature and may have been missed using correlation coefficients. Digital signal processing tools can be embedded and enhance existing clustering algorithms.