Parallel e-CCC-Biclustering: Mining Approximate Temporal Patterns in Gene Expression Time Series Using Parallel Biclustering

2012 ◽  
pp. 21-31 ◽  
Author(s):  
Filipe Cristóvão ◽  
Sara C. Madeira
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Temporal Patterns ◽  
Gene Expression Time Series ◽  
Expression Time

Estimation and inversion of the effects of cell population asynchrony in gene expression time-series

2003 ◽  
Vol 83 (4) ◽  
pp. 835-858 ◽  
Author(s):  
Harri Lähdesmäki ◽  
Heikki Huttunen ◽  
Tommi Aho ◽  
Marja-Leena Linne ◽  
Jari Niemi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Cell Population ◽  
Gene Expression Time Series ◽  
And Inversion ◽  
Expression Time

scholarly journals BTW: a web server for Boltzmann time warping of gene expression time series

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W482-W485 ◽  
Author(s):  
F. Ferre ◽  
P. Clote
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Web Server ◽  
Time Warping ◽  
Gene Expression Time Series ◽  
Expression Time

scholarly journals Stochastic Dynamic Modeling of Short Gene Expression Time-Series Data

2008 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Zidong Wang* ◽  
Fuwen Yang ◽  
Daniel W. C. Ho ◽  
Stephen Swift ◽  
Allan Tucker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Dynamic Modeling ◽  
Time Series Data ◽  
Series Data ◽  
Stochastic Dynamic ◽  
Gene Expression Time Series ◽  
Expression Time

scholarly journals NETWORKS FROM GENE EXPRESSION TIME SERIES: CHARACTERIZATION OF CORRELATION PATTERNS

2007 ◽  
Vol 17 (07) ◽  
pp. 2477-2483 ◽  
Author(s):  
D. REMONDINI ◽  
N. NERETTI ◽  
C. FRANCESCHI ◽  
P. TIERI ◽  
J. M. SEDIVY ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Single Gene ◽  
Biological Information ◽  
Reconstruction Method ◽  
Gene Expression Time Series ◽  
Expression Time

We address the problem of finding large-scale functional and structural relationships between genes, given a time series of gene expression data, namely mRNA concentration values measured from genetically engineered rat fibroblasts cell lines responding to conditional cMyc proto-oncogene activation. We show how it is possible to retrieve suitable information about molecular mechanisms governing the cell response to conditional perturbations. This task is complex because typical high-throughput genomics experiments are performed with high number of probesets (103–104 genes) and a limited number of observations (< 102 time points). In this paper, we develop a deepest analysis of our previous work [Remondini et al., 2005] in which we characterized some of the main features of a gene-gene interaction network reconstructed from temporal correlation of gene expression time series. One first advancement is based on the comparison of the reconstructed network with networks obtained from randomly generated data, in order to characterize which features retrieve real biological information, and which are instead due to the characteristics of the network reconstruction method. The second and perhaps more relevant advancement is the characterization of the global change in co-expression pattern following cMyc activation as compared to a basal unperturbed state. We propose an analogy with a physical system in a critical state close to a phase transition (e.g. Potts ferromagnet), since the cell responds to the stimulus with high susceptibility, such that a single gene activation propagates to almost the entire genome. Our result is relative to temporal properties of gene network dynamics, and there are experimental evidence that this can be related to spatial properties regarding the global organization of chromatine structure [Knoepfler et al., 2006].

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