ArticleJournal of computational biology : a journal of computational molecular cell biology2010
Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.BMC bioinformatics · 2020Article
- A block mixture model to map eQTLs for gene clustering and networking.Scientific reports · 2016Article
- A bi-Poisson model for clustering gene expression profiles by RNA-seq.Briefings in bioinformatics · 2014Article
- A computational algorithm for functional clustering of proteome dynamics during development.Current genomics · 2014Article
- How to cluster gene expression dynamics in response to environmental signals.Briefings in bioinformatics · 2012Article
- Predicting housekeeping genes based on Fourier analysis.PloS one · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Functional gene clustering is a statistical approach for identifying the temporal patterns of gene expression measured at a series of time points. By integrating wavelet transformations, a power dimension-reduction technique, noisy gene expression data is smoothed and clustered allowing for new patterns of functional gene expression profiles to be identified. We implement the idea of wavelet dimension reduction into the mixture model for gene clustering, aimed to de-noise the data by transforming an inherently high-dimensional biological problem to its tractable low-dimensional representation. As a first attempt of its kind, we capitalize on the simplest Haar wavelet shrinkage technique to break an original signal down into its spectrum by taking its averages and differences and, subsequently, detect gene expression patterns that differ in the smooth coefficients extracted from noisy time series gene expression data. The method is shown to be effective on simulated data and and on recent time course gene expression data. Supplementary Material is available at www.liebertonline.com .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.