A multi-step approach to time series analysis and gene expression clustering
- PMID: 16397005
- DOI: 10.1093/bioinformatics/btk026
A multi-step approach to time series analysis and gene expression clustering
Abstract
Motivation: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation.
Results: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters.
Availability: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author.
Supplementary information: Supplementary data are available at Bioinformatics online.
Similar articles
-
An overview of Spotfire for gene-expression studies.Curr Protoc Bioinformatics. 2004 Sep;Chapter 7:Unit 7.7. doi: 10.1002/0471250953.bi0707s6. Curr Protoc Bioinformatics. 2004. PMID: 18428733 Review.
-
GOurmet: a tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions.BMC Bioinformatics. 2006 Mar 17;7:151. doi: 10.1186/1471-2105-7-151. BMC Bioinformatics. 2006. PMID: 16545118 Free PMC article.
-
Visualization methods for statistical analysis of microarray clusters.BMC Bioinformatics. 2005 May 12;6:115. doi: 10.1186/1471-2105-6-115. BMC Bioinformatics. 2005. PMID: 15890080 Free PMC article.
-
PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order.Bioinformatics. 2005 Apr 1;21(7):1280-1. doi: 10.1093/bioinformatics/bti141. Epub 2004 Nov 16. Bioinformatics. 2005. PMID: 15546938
-
Software packages for quantitative microarray-based gene expression analysis.Curr Pharm Biotechnol. 2003 Dec;4(6):417-37. doi: 10.2174/1389201033377436. Curr Pharm Biotechnol. 2003. PMID: 14683435 Review.
Cited by
-
Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms.BMC Med Genomics. 2019 Aug 5;12(1):117. doi: 10.1186/s12920-019-0562-z. BMC Med Genomics. 2019. PMID: 31382962 Free PMC article.
-
Using microRNA Networks to Understand Cancer.Int J Mol Sci. 2018 Jun 26;19(7):1871. doi: 10.3390/ijms19071871. Int J Mol Sci. 2018. PMID: 29949872 Free PMC article. Review.
-
Circulating miRNAs in sepsis-A network under attack: An in-silico prediction of the potential existence of miRNA sponges in sepsis.PLoS One. 2017 Aug 18;12(8):e0183334. doi: 10.1371/journal.pone.0183334. eCollection 2017. PLoS One. 2017. PMID: 28820886 Free PMC article.
-
A novel parametric approach to mine gene regulatory relationship from microarray datasets.BMC Bioinformatics. 2010 Dec 14;11 Suppl 11(Suppl 11):S15. doi: 10.1186/1471-2105-11-S11-S15. BMC Bioinformatics. 2010. PMID: 21172050 Free PMC article.
-
Reverse engineering large-scale genetic networks: synthetic versus real data.J Genet. 2010 Apr;89(1):73-80. doi: 10.1007/s12041-010-0013-2. J Genet. 2010. PMID: 20505249
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
