ArticleEURASIP journal on bioinformatics & systems biology2009
Clustering of gene expression data based on shape similarity.
Article in EURASIP journal on bioinformatics & systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dose-response-defined physiological framework enables comparative analysis of transcriptional response dynamics across multiple herbicides inFrontiers in plant science · 2026Article
- Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression.EJIFCC · 2023Article
- Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs).Scientific reports · 2021Article
- On the selection of appropriate distances for gene expression data clustering.BMC bioinformatics · 2014Article
- Frequency-based time-series gene expression recomposition using PRIISM.BMC systems biology · 2012Article
- A topology-based metric for measuring term similarity in the gene ontology.Advances in bioinformatics · 2012Article
- Nonlinear gene cluster analysis with labeling for microarray gene expression data in organ development.BMC proceedings · 2011Article
- Measuring similarity between gene expression profiles: a Bayesian approach.BMC genomics · 2009Article
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Authors and funding
2 authors.
Funding
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Abstract
A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which then are subject to a variational Bayes clustering plus a non-Bayesian (Silhouette) cluster statistic. The statistic shows an improved ability to identify the correct number of clusters and assign the components of cluster. Based on initial results for both generated test data and Escherichia coli microarray expression data and initial validation of the Escherichia coli results, it is shown that the method has promise in being able to better cluster time-series microarray data according to shape similarity.
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Registered trials
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