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Literature DB >> 21552424

Bayesian model-based tight clustering for time course data.

Abstract

Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering method for time course gene expression data, which selects a small number of closely-related genes and constructs tight clusters only with these closely-related genes.

Entities: Chemical Disease Gene Species

Year: 2010 PMID： 21552424 PMCID： PMC3087980 DOI： 10.1007/s00180-009-0159-7

Source DB: PubMed Journal: Comput Stat ISSN： 0943-4062 Impact factor: 1.000

15 in total

1. A mixture model-based approach to the clustering of microarray expression data.

Authors: G J McLachlan; R W Bean; D Peel
Journal: Bioinformatics Date: 2002-03 Impact factor: 6.937

2. Mixture modelling of gene expression data from microarray experiments.

Authors: Debashis Ghosh; Arul M Chinnaiyan
Journal: Bioinformatics Date: 2002-02 Impact factor: 6.937

3. Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

Authors: A V Lukashin; R Fuchs
Journal: Bioinformatics Date: 2001-05 Impact factor: 6.937

10. Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering.

Authors: Kazumi Hakamada; Masahiro Okamoto; Taizo Hanai
Journal: Bioinformatics Date: 2006-01-24 Impact factor: 6.937

1 in total

1. Finding gene clusters for a replicated time course study.

Authors: Li-Xuan Qin; Linda Breeden; Steven G Self
Journal: BMC Res Notes Date: 2014-01-24

1 in total

Bayesian model-based tight clustering for time course data.

1. A mixture model-based approach to the clustering of microarray expression data.

2. Mixture modelling of gene expression data from microarray experiments.

3. Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

4. Comparisons and validation of statistical clustering techniques for microarray gene expression data.

5. Gaussian mixture clustering and imputation of microarray data.

6. Using hidden Markov models to analyze gene expression time course data.

7. Statistical tests for identifying differentially expressed genes in time-course microarray experiments.

8. Tight clustering: a resampling-based approach for identifying stable and tight patterns in data.

9. A mixture model with random-effects components for clustering correlated gene-expression profiles.

10. Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering.

1. Finding gene clusters for a replicated time course study.