Skip to main content
Article thumbnail
Location of Repository

A comparison of four clustering methods for brain expression microarray data

By Alexander L Richards, Peter Holmans, Michael C O'Donovan, Michael J Owen and Lesley Jones
Topics: Research Article
Publisher: BioMed Central
OAI identifier:
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles


    1. (2002). AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.
    2. (2005). AJ: Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks.
    3. (2005). Allison DB: Reproducible Clusters from Microarray Research: Whither?
    4. (2006). B: An online database for brain disease research.
    5. (2002). Barkai N: Revealing modular organization in the yeast transcriptional network. Nature Genetics
    6. (2008). Barres BA: A transcriptome database for astrocytes, neurons and oligodendrocytes: a new resource for understanding brain development and function.
    7. (2006). Clustering microarray gene expression data using weighted Chinese restaurant process. Bioinformatics
    8. Databank: Brain Tissue Gene Expression Repository.
    9. (2004). Differential expression of mitogen-activated protein kinases and immediate early genes fos and jun in thalamus in schizophrenia.
    10. (2004). Distributed computing in practice: the Condor experience. Concurrency and Computation: Practice and Experience
    11. (2005). Draghici S: Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics
    12. (2003). Fuzzy C-means method for clustering microarray data. Bioinformatics
    13. (2005). Hénaut A: Comments on selected fundamental aspects of microarray analysis.
    14. (2002). Hogenesch JB: Large-scale analysis of the human and mouse transcriptomes.
    15. (2003). Iterative signature algorithm for the analysis of large-scale gene expression data.
    16. (2005). Meng F: Evolving gene/ transcript definitions significantly alter the interpretation of Gene Chip data. Nucleic Acids Research
    17. (2006). Microarray Data Analysis: from disarray to consolidation to consensus. Nature Reviews Genetics
    18. (2004). Miyano S: Open Source Clustering Software. Bioinformatics
    19. (2006). NCBI GEO: mining tens of millions of expression profiles – database and tools update.
    20. (2005). NS: Finding regulatory modules through large-scale gene expression analysis. Bioinformatics
    21. (2007). Penalized and weighted K-means for clustering with scattered objects and prior information in highthroughput biological data. Bioinformatics
    22. Project for Statistical Computing []. R: A language and environment for statistical computing
    23. (2008). Rare Structural Variants Disrupt Multiple Genes in Neurodevelopmental Pathways in Schizophrenia. Science
    24. (2005). Searching for differentially expressed gene combinations. Genome Biology
    25. (2004). Speed TP: GOstat: Find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics
    26. (2003). TP: A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics
    27. (2006). Tseng GC: Evaluation and comparison of gene clustering methods in microarray analysis. Bioinformatics
    28. vegan: Community Ecology Package,
    29. (2007). VM: Gene expression profiles in rat brain disclose CNS signature genes and regional patterns of functional specialisation.
    30. (1979). Wong MA: A K-Means Clustering Algorithm. Applied Statistics
    31. (2006). Y-X: Comparisons of Graphstructure Clustering Methods for Gene Expression Data. Acta Biochimica et Biophysica Sinica
    32. (2006). Yang PC: CRSD: a comprehensive web server for composite regulatory signature discovery. Nucleic Acids Research
    33. (2006). Zitzer E: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.