Literature DB >> 16221984

Identifying genes from up-down properties of microarray expression series.

Karen Willbrand1, Francois Radvanyi, Jean-Pierre Nadal, Jean-Paul Thiery, Thomas M A Fink.   

Abstract

MOTIVATION: We consider any collection of microarrays that can be ordered to form a progression; for example, as a function of time, severity of disease or dose of a stimulant. By plotting the expression level of each gene as a function of time, or severity, or dose, we form an expression series, or curve, for each gene. While most of these curves will exhibit random fluctuations, some will contain a pattern, and these are the genes that are most likely associated with the quantity used to order them.
RESULTS: We introduce a method of identifying the pattern and hence genes in microarray expression curves without knowing what kind of pattern to look for. Key to our approach is the sequence of ups and downs formed by pairs of consecutive data points in each curve. As a benchmark, we blindly identified genes from yeast cell cycles without selecting for periodic or any other anticipated behaviour. CONTACT: tmf20@cam.ac.uk SUPPLEMENTARY INFORMATION: The complete versions of Table 2 and Figure 4, as well as other material, can be found at http://www.lps.ens.fr/~willbran/up-down/ or http://www.tcm.phy.cam.ac.uk/~tmf20/up-down/

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Mesh:

Year:  2005        PMID: 16221984     DOI: 10.1093/bioinformatics/bti549

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

1.  Diverse correlation patterns between microRNAs and their targets during tomato fruit development indicates different modes of microRNA actions.

Authors:  Sara Lopez-Gomollon; Irina Mohorianu; Gyorgy Szittya; Vincent Moulton; Tamas Dalmay
Journal:  Planta       Date:  2012-08-26       Impact factor: 4.116

2.  Construction and use of gene expression covariation matrix.

Authors:  Jérôme Hennetin; Petri Pehkonen; Michel Bellis
Journal:  BMC Bioinformatics       Date:  2009-07-13       Impact factor: 3.169

3.  Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae.

Authors:  Frank Emmert-Streib; Matthias Dehmer
Journal:  BMC Syst Biol       Date:  2009-07-20

4.  Predicting cell cycle regulated genes by causal interactions.

Authors:  Frank Emmert-Streib; Matthias Dehmer
Journal:  PLoS One       Date:  2009-08-18       Impact factor: 3.240

5.  The more the merrier: comparative analysis of microarray studies on cell cycle-regulated genes in fission yeast.

Authors:  Samuel Marguerat; Thomas S Jensen; Ulrik de Lichtenberg; Brian T Wilhelm; Lars J Jensen; Jürg Bähler
Journal:  Yeast       Date:  2006-03       Impact factor: 3.239

6.  Cyclebase.org--a comprehensive multi-organism online database of cell-cycle experiments.

Authors:  Nicholas Paul Gauthier; Malene Erup Larsen; Rasmus Wernersson; Ulrik de Lichtenberg; Lars Juhl Jensen; Søren Brunak; Thomas Skøt Jensen
Journal:  Nucleic Acids Res       Date:  2007-10-16       Impact factor: 16.971

7.  Extracting binary signals from microarray time-course data.

Authors:  Debashis Sahoo; David L Dill; Rob Tibshirani; Sylvia K Plevritis
Journal:  Nucleic Acids Res       Date:  2007-05-21       Impact factor: 16.971

8.  Input-output maps are strongly biased towards simple outputs.

Authors:  Kamaludin Dingle; Chico Q Camargo; Ard A Louis
Journal:  Nat Commun       Date:  2018-02-22       Impact factor: 14.919

  8 in total

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